Initial Parameter Values Research Articles

Robust solution of coordinate transformation parameters with a high breakdown point

Because the M estimation method may lead to poor robustness or even failure owing to excessive outliers, a robust algorithm with a high breakdown point was proposed and applied to the estimation of coordinate transformation parameters. Firstly, the sampling method was used to calculate multiple sets of model parameters, and some sampling results were sifted according to posterior information. Then, the samples were sorted according to their number in the sampling results, and the F-test was adopted to screen and reserve valid information. Finally, the initial values of the reliable parameters were computed using the valid information, and the final parameters were obtained by the Institute of Geodesy and Geophysics III scheme. Monte Carlo method was adopted for the simulation test, and a case analysis was chosen for verification. The results show that the proposed method can identify and process outliers more accurately than those of Rousseeuw and Hubert (2011 Wires Data Min. Knowl. 1 73–79) and Tao et al (2016 Acta Geod. Cartogr. Sin. 45 297–301). When the proportion of outliers exceeded 50%, the proposed algorithm maintained a strong robustness and had a high breakdown point.

Fast and practical method for underwater stereo vision calibration based on ray-tracing.

The perspective camera model has difficulty handling refracted light in the underwater environment. To achieve accurate and convenient calibration in large underwater scenes, we propose a method based on the underwater refractive camera model in this paper. First, the initial values of the refraction parameters are solved using refraction coplanarity constraints. Then the initial values are optimized nonlinearly using co-point constraints, which simplifies the optimization process of existing methods. In the field of view of 200m m×200m m, the experiment results show that the reconstruction accuracy of the proposed method can reach below 0.02mm, and it is equally effective in the case of sparse calibration.

An extended SEIRDV compartmental model: case studies of the spread of COVID-19 and vaccination in Tunisia and South Africa.

In the wake of the unprecedented health crisis triggered by the global COVID-19 pandemic, countries are still grappling with the pandemic's immediate health and socioeconomic consequences. This paper presents an extended SEIRD model with vaccination to study the evolution of COVID-19 in South Africa and Tunisia since the commencement of the vaccination campaign in each country, respectively. Epidemiologists often quantify a risk reduction following the implementation of non-pharmaceutical containment measures and vaccines when attempting to stem the spread of pandemics. However, an important question they often ask is the effectiveness of the non-pharmaceutical containment measures (social distancing and lockdowns) and the efficacy of such measures, including vaccines. Africa's COVID-19 vaccine roll-out stands at 16% as of April 2022; however, the continent lags behind many developing countries even though it harbours about 16% of the world population. While proliferating literature quantifies the efficacy and effectiveness of COVID-19 vaccines, very little has been done using the SEIRDV model in African countries. This study compares the model-predicted results with the available data to estimate the dynamics of the infected population, using data from 20-03-2021 to 30-12-2021. A simulation of the SEIRDV model is performed and fitted to the data. Simulating the model involves solving a system of Ordinary Differential Equations numerically by taking the initial values for the key model parameters as inputs. After simulating the SEIRDV model, the model parameters are compared with real-world COVID-19 and vaccination data in order to estimate the values of the different parameters that best fit the observed data. The results of the study showed an inverted U-shaped trend for the infection rate after vaccination, indicating that increasing the vaccination rates reduces the transmission rates. Therefore African countries must continue to scale up the vaccination campaigns, and the world needs to endeavour to ensure an equitable vaccination roll-out to developing countries.

Anisotropy safety potential field model under intelligent and connected vehicle environment and its application in car-following modeling

Potential field theory, as a theory that can also be applied to vehicle control, is an emerging risk quantification approach to accommodate the connected and self-driving vehicle environment. Vehicles have different risk impact effects on other road participants in each direction under the influence of road rules. This variability exhibited by vehicles in each direction is not considered in the previous potential field model. Therefore, this paper proposed a potential field model that takes the anisotropy of vehicle impact into account: (1) introducing equivalent distances to separate the potential field area in the different directions before and after the vehicle; (2) introducing co-virtual forces to characterize the effect of the side-by-side travel phenomenon on vehicle car-following travel; (3) introducing target forces and lane resistance, which regress the control of desired speed to control the acceptable risk of drivers. The Next Generation Simulation (NGSIM) dataset is used in this study to create the model's initial parameter values based on the artificial swarm algorithm. The simulation findings indicate that when the vehicle is given the capacity to perceive the surrounding traffic environment, the suggested the anisotropic safety potential field model (ASPFM) performs better in terms of driving safety.

Efficient prestack time migration velocity analysis: A correlation‐based global optimization approach

AbstractAn accurate migration velocity model is vital to seismic imaging. Conventional time‐domain migration velocity analysis techniques commonly obtain the velocity model by iteratively picking the velocity spectra of common reflection point gathers or scanning selection, which is usually laborious and computationally expensive. To solve these issues, we propose a more effective migration velocity analysis method for prestack time migration. This method is based on cross‐correlation stacked time shift functions of local migrated gathers and uses the very fast simulated annealing algorithm to semi‐automatically invert the migration velocity parameters. This new correlation‐based objective function can catch subtle bending features of the reflector in the local migrated gather. The proposed approach applies an efficient global optimization algorithm that does not depend on the initial parameter value and is easy to extend to the multiparameter complex case. Moreover, the strategy of using local migrated gathers can incorporate prior information in the inversion to avoid the effects of misidentifying reflection events and the interference of multiples. Furthermore, applying a dynamic parameter constraint strategy for the very fast simulated annealing algorithm improves the convergence speed. Both synthetic and real data examples demonstrate that the proposed migration velocity analysis method can efficiently build an accurate time migration velocity model, which can also provide a good initial velocity model for depth migration.

Bayesian model selection considering model complexity using stochastic filtering

AbstractThis paper proposes a Bayesian model selection framework to determine optimal, parsimonious models for given building structures under ground motion excitation using stochastic filtering. Structural system identification at a regional scale after an earthquake is nominally a monumental task due to its inherently high computational cost. This challenge is currently addressed in the literature by using simplified structural models that bear analytical solutions, such as Timoshenko or shear beams, and shear building models. However, a low computational effort usually leads to an increased prediction error and consequently, a higher model uncertainty. This poses the dilemma of prediction accuracy versus model complexity when selecting a model class to represent a building structure. The proposed framework selects the model class that strikes the best balance between the two. To this end, the notion of cumulative evidence is introduced here as the integral of local evidence over the ground motion duration, which is then formulated as the difference between the cumulative likelihood of the observed measurements and the cumulative penalty. The likelihood measure promotes models whose predictions better match the observations. This is counteracted by the penalty, which is devised as an “Ockham factor,” and penalizes models with higher information gains due to their higher complexities, for example, a larger number of parameters. The proposed approach also yields the best tuning parameters of the stochastic filter for each model class and the best initial values of the identification parameters. The proposed framework is verified using a synthetic example, and validated using recorded data from the Millikan Library building in California and the ANX building in Japan.

A parameter identification and state of charge estimation method of lithium-ion battery considering temperature bias

To eliminate the impact of inaccurate initial parameter value on the parameter identification results of lithium-ion battery (LIB) model, a method for parameter identification of LIB combining Matlab and 1stOpt is proposed, fully utilizing the powerful global optimization ability of 1stOpt to obtain accurate initial parameter value. Moreover, this method can also efficiently heighten the precision of parameter identification results. The hybrid pulse power characteristic (HPPC) experiment is carried out on a LIB in the laboratory to obtain the relationship among state of charge (SOC), open circuit voltage (OCV), and ambient temperature, and the data required for parameter identification. To enhance the accuracy of SOC estimation, based on the obtained high-precision identification parameters, a dual-polarization dynamic thermal model of coupling temperature and SOC of the LIB is established. We find that the battery temperature tested is inconsistent with the ambient temperature, which causes inaccurate temperature input of the battery model, thus affecting the accuracy of the battery model. In this work, a particle swarm optimization (PSO) algorithm is introduced to obtain exact temperature differences to rectify the temperature input of the battery model. Then, the simulated terminal voltage of the battery is compared and validated against the experimental data. It is obtained that the maximum and minimum average errors considering temperature bias are only 0.962 % and 0.0075 %, respectively, while the maximum and minimum average errors without temperature bias are 3.622 % and 0.109 %, respectively, which commendably prove the accuracy of the established model. Finally, the extended Kalman filter (EKF) is adopted to estimate the SOC under the dynamic stress test (DST), and the maximum error is less than 0.8 %. The results strongly show that the established model in this work still has high accuracy in a wide temperature range. The results provide a theoretical reference for the related research on parameter identification and modeling of batteries.

Exploring an Innovative Moral Education Cultivation Model in Higher Education through Neural Network Perspective: A Preliminary Study

ABSTRACT This paper aims to enhance the effectiveness of moral education teaching through an innovative approach. By integrating neural network technology, a data-model-driven three-dimensional initial model construction algorithm was proposed to identify the characteristics of moral education classroom teaching. The 3D space base point and initial value of shape parameters were obtained based on color point cloud data, and the 3D space coordinates of all the vertices of the model were calculated using the Euclidean distance algorithm, resulting in the construction of an initial 3D model. Furthermore, a model of Innovative Moral Education (IME) in higher education was developed based on neural network, and a framework analysis was conducted. Results from simulation teaching research indicate that the IME teaching mode based on the proposed neural network model has a significant practical effect. Overall, this study offers insights into an innovative approach to moral education teaching and contributes to the development of effective pedagogical practices.

An investigation of periodic degradation of axle box vibration spectrum for a high-speed rail vehicle based on Bayesian method

Axle box vibration serves as the main source of excitations for rail vehicles. Due to the wear of wheel/rail contact and the re-profiling procedure, the axle box vibration usually degrades periodically with the increased mileage in the service. This could significantly impact the estimation of vibration fatigue when the component is subjected to the axle box vibration. This paper develop a method to describe the periodic evolution of axle box vibration spectrum to better characterise the vibration spectrum of axle box. In this study, a Wiener process incorporating with four random parameters was employed to model the non-linearity of the degradation process. The maximum likelihood estimation (MLE) algorithm is used to estimate the initial values of the random parameters, and a Bayesian approach is employed to update the parameters based on newly obtained data. Finally, the proposed methodology is tested using long-term field test data from a high-speed train, and the results demonstrate that it accurately estimates the evolution of the axle box acceleration spectral density (ASD) spectrum. This could aid in predicting the residual service life of structures subjected to axle box vibration and further contribute to the development of maintenance strategies and top-down design of the structure.

Application of Fractional SIQRV Model for SARS-CoV-2 and Stability Analysis

Throughout history, infectious diseases have been the cause of outbreaks and the deaths of people. It is crucial for endemic disease management to be able to forecast the number of infections at a given moment and the frequency of new infections so that the appropriate precautions can be taken. The COVID-19 pandemic has highlighted the value of mathematical modeling of pandemics. The susceptible–infected–quarantined–recovered–vaccinated (SIQRV) epidemic model was used in this work. Symmetrical aspects of the proposed dynamic model, disease-free equilibrium, and stability were analyzed. The symmetry of the population size over time allows the model to find stable equilibrium points for any parameter value and initial conditions. The assumption of the strong symmetry of the initial conditions and parameter values plays a key role in the analysis of the fractional SIQRV model. In order to combat the pandemic nature of the disease, control the disease in the population, and increase the possibility of eradicating the disease, effective control measures include quarantine and immunization. Fractional derivatives are used in the Caputo sense. In the model, vaccination and quarantine are two important applications for managing the spread of the pandemic. Although some of the individuals who were vaccinated with the same type and equal dose of vaccine gained strong immunity thanks to the vaccine, the vaccine could not give sufficient immunity to the other part of the population. This is thought to be related the structural characteristics of individuals. Thus, although some of the individuals vaccinated with the same strategy are protected against the virus for a long time, others may become infected soon after vaccination. Appropriate parameters were used in the model to reflect this situation. In order to validate the model, the model was run by taking the COVID-19 data of Türkiye about a year ago, and the official data on the date of this study were successfully obtained. In addition to the stability analysis of the model, numerical solutions were obtained using the fractional Euler method.

Математическое моделирование процесса полимеризации этилена в автоклавном реакторе с мешалкой

Building a mathematical model of the polymerization reactor is necessary to determine the dependencies between the input variables, the technological mode and the output variables of the ethylene polymerization process in an autoclave reactor with a stirrer, as well as to analyze the effect of the reactor design parameters on efficiency. A deterministic mathematical model developed on the basis of analysis of the physico-chemical regularities of the ethylene polymerization process is proposed to solve these problems. There is shown a schematic diagram of ethylene polymerization indicating the flows of substances and energy, a description of the process, the mechanism of chemical kinetics of ethylene polymerization, a system of assumptions simplifying the construction of a mathematical model, equations of a mathematical model of an ethylene polymerization reactor in an autoclave reactor with a stirrer. The autoclave ethylene polymerization reactor is presented as a cascade of ideal mixing apparatus to describe each of the reactor zones. The rates of chemical reactions of initiation, growth and chain breakage are expressed in accordance with the law of acting masses. The mathematical model in its final form is a system of ordinary differential equations for describing each of the reactor zones. The initial numerical values of variables and parameters for modeling ethylene polymerization in the autoclave reactor are given. The results of numerical experiments are shown, and the influence of various factors on the output variables of the ethylene polymerization process is investigated. The proposed mathematical model can be used to optimize the technological regime of the ethylene polymerization process in an autoclave reactor with a stirrer, as well as to analyze the influence of the design parameters of the reactor on its efficiency.

Adaptive maximum power point tracking based on Kalman filter for hydrogen fuel cell in hybrid unmanned aerial vehicle applications

In this paper, an adaptive real-time estimation method based on Kalman filter is proposed for tracking the maximum power point (MPP) of a hydrogen fuel cell (FC) in hybrid unmanned aerial vehicle (UAV) applications. To achieve the adaptive MPP tracking (MPPT), a mathematical model for the hydrogen FC is established. Then, the recursive least square method is employed to identify the initial values of model parameters. On this basis, the MPP of the hydrogen FC under steady conditions can be derived. Furthermore, the state and observation equations based on Kalman filter are introduced to adaptively estimate the model parameters in real-time. Moreover, the real-time model parameters would be used to optimize the MPP in accordance with the operating conditions such that the adaptive MPPT can be achieved. Finally, various simulations and experiments are conducted to verify the effectiveness and accuracy of the adaptive MPPT for the hydrogen FC in hybrid UAV applications. Results show that the adaptive MPPT can not only track the MPP accurately in real-time, but also reduce the oscillation of the hydrogen FC. Compared with the MPPT methods based on perturb and observe (P&O) and particle swarm optimization (PSO), the maximum power tracking error of the adaptive MPPT can be improved by 2.83% and 1.10%, respectively.

An adaptive multivariate Student's t‐process recursive method for hypersonic glide vehicle trajectory prediction

AbstractThe trajectory prediction of the hypersonic glide vehicle (HGV) can provide hit point information for early warning systems, which is of great significance for near‐space defence operations. However, the heavy‐tailed noise caused by abnormal environmental disturbance and abrupt changes in vehicle trajectory seriously affects the accuracy of HGV trajectory prediction. To solve the problem of trajectory prediction for HGV under heavy‐tailed noise, we propose an adaptive multivariate Student's t‐process regression method called aEM‐MVTPR. Firstly, a heavy‐tailed noise model is developed using the Student's t‐distribution. Secondly, a multivariate Student's t‐process regression (MVTPR) for HGV trajectory prediction is derived, and the method can learn the HGV trajectory time series features and mine the correlations among the trajectory variables. Finally, to further improve the method's robustness, we use the accelerated expectation maximisation algorithm and Pearson correlation analysis to adaptively estimate and adjust the initial values of the MVTPR parameters. The simulation experimental results show that the proposed method has more accurate predicted values of trajectory variables than multivariate Gaussian process regression (MVGPR) under heavy‐tailed noise conditions. In addition, the ability of the aEM‐MVTPR to adaptively adjust the parameter makes it more robust than the MVTPR under different noise environments.

Intuitionistic fuzzy optimization method for solving multi-objective linear fractional programming problems

An iterative technique based on the use of parametric functions is proposed in this paper to obtain the best preferred optimal solution of a multi-objective linear fractional programming problem (MOLFPP). Each fractional objective is transformed into a non-fractional parametric function using certain initial values of parameters. The parametric values are iteratively calculated and the intuitionistic fuzzy optimization method is used to solve a multi-objective linear programming problem. Also, some basic properties and operations of an intuitionistic fuzzy set are considered. The development of the proposed algorithm is based on the principle of optimal decision set achieved by the intersection of various intuitionistic fuzzy decision sets which are obtained corresponding to each objective function. Additionally, as the intuitionistic fuzzy optimization method utilizes the degree of belonging and degree of non-belonging, we used the linear membership function for belonging and non-belonging to see its impact on optimization and to get insight into such an optimization process. The proposed approaches have been illustrated with numerical examples.

Fuzzy Hazard Rate Function for Mixed Exponential and Maxwell Distribution

In this paper, the mixed probability distribution, from mixing exponential and Maxwell distribution is constructed, were the mixed probability distribution is found and also the C.D.F. and Reliability functions, are derived. The parameters (α,θ) of the distribution are estimated by methods of Maximum likelihood, Moments and Regression method, the comparison was done by simulation method, the initial values of parameters are (ki=0.3,0.6) ,and different sample sizes of (small n= 25, medium 50, and large 75). All the results of the comparison of fuzzy Hazard rate function were detailed in tables, The results of the comparison indicated that the Moments method is the most accurate, followed by maximum Likelihood, and Regression.

Genetic Algorithm Based Clustering Optimization A Survey

Genetic algorithm has an important role in improving clustering methods. When it comes to getting into a dataset, one of the most common methods used is clustering. Recent years have seen a rise in the number of articles interested in clustering, which may be attributed to the development of various new fields of application. Most clustering method's performance depends on initial values of parameters or the preprocessing of a dataset, so clustering needs to optimize. The improvement of clustering in two directions, either we improve the parameters or improve the input data. Ga is an evolutionary technique well-known in optimization. This survey deals with the methods that use a genetic algorithm to choose parameter values and input data. This study shows many important performance metrics used to get the optimal result in each research.

Coexistence of Hidden Attractors in the Smooth Cubic Chua’s Circuit with Two Stable Equilibria

Since the invention of Chua’s circuit, numerous generalizations based on the substitution of the nonlinear function have been reported. One of the generalizations is obtained by substituting cubic nonlinearity for piece-wise linear (PWL) nonlinearity. Although hidden chaotic attractors with a PWL nonlinearity have been discovered in the classical Chua’s circuit, chaotic attractors with a smooth cubic nonlinearity have long been known as self-excited attractors. Through a systematically exhaustive computer search, this paper identifies coexisting hidden attractors in the smooth cubic Chua’s circuit. Either self-excited or coexisting hidden attractors are now possible in the smooth cubic Chua’s circuit with algebraically elegant values of both initial points and system parameters. The newly found coexisting attractors exhibit an inversion symmetry. Both initial points and system parameters are equally required to localize hidden attractors. Basins of attraction of individual equilibria are illustrated and clearly show critical areas of multistability where a tiny drift in an initial point potentially induces jumps among different basins of attraction and coexisting states. Such multistability poses potential threats to engineering applications. The dynamical regions of hidden and self-excited attractors, and areas of stability of equilibria, are illustrated against two parameter spaces. Both illustrations reveal that two nonzero equilibrium points of the smooth cubic Chua’s circuit have a transition from unstable to stable equilibrium points, leading to generations of self-excited and hidden attractors simultaneously.

Enhanced technique for photochemical nano-polishing of a rough quartz surface: the numerical calculation of profile evolution.

The enhanced technique of quartz surface nano-local etching is considered. The enhancement of an evanescent field above surface protrusions and, as a result, an increase in the rate of quartz nano-local etching, are proposed. The possibility to reduce the amount of etch products filled in rough surface troughs and control the optimal rate of the surface nano-polishing process is achieved. The dependences of the quartz surface profile evolution on the initial values of surface roughness parameters, on the refractive index of the medium containing molecular chlorine and contacting the quartz surface, and on the wavelength of radiation illuminating this surface are shown.

Development of methodology for scenario analysis of investment projects of enterprises of the mineral resource complex

Theoretical and applied aspects of scenario analysis of investment projects of enterprises in the mineral resource sector of the economy are considered, its advantages and disadvantages are analyzed. Taking into account the organizational and economic features of mineral resources management, a number of new modifications of the scenario analysis method, aimed at solving an urgent problem - reducing the information uncertainty in assessing the expected efficiency and risk of investment projects, are proposed. The peculiarity of the proposed new modifications is the use of the interval-probabilistic approach in the implementation of the scenario analysis procedure. This approach is based on a moderately pessimistic system of preferences in obtaining point values of the investment project initial parameters. Fishburn estimates and the hierarchy analysis method were used to reduce subjective uncertainty. The maximum likelihood values in the sense of the maximum a priori probability are used as expected estimates. An additional indicator of risk assessment, which characterizes the probability of the event that the net present value of the project will take a value less than the specified one, is proposed. When analyzing one project, this indicator is more informative than the standard deviation. A statistical hypothesis was tested on the improvement of the validity of investment decisions developed using the modified scenario analysis method compared to the standard method.

Tri‐band bandpass filter designed by a fast hybrid approach

AbstractMultiband bandpass filter is a key component in multistandard wireless communication system. This paper proposes a novel tri‐band bandpass filter (TB‐BPF) which consists of two mesh‐type multimode resonators (MT‐MMR) and two stub‐loaded U‐type feedlines. The MT‐MMR is formed by parallel connecting two open‐end stubs and one mesh‐type structure. Such specific configuration is featured by four inherent transmission zeros. The in‐depth analysis on the generation mechanism of the transmission zeros is made, and then a fast hybrid approach is developed for design procedure. Some explicit design equations are presented for fast determining initial values of some key parameters, and then further optimized by using a constraint condition, which effectively accelerate design process. For verification, an example of the TB‐BPF was designed fabricated and measured. Its center frequencies are at 3.16, 4.27, and 5.70 GHz, respectively. Six transmission zeros are generated for sharp frequency selectivity, and are at 1.46, 3.58, 3.86, 4.73, 5.51, and 7.18 GHz, respectively. Its size is about 0.59 × 0.19 λg2.