Set Of Initial Values Research Articles

Calculation of the stress-strain state of layers of cross-ply laminate based on an experimental stress-strain curves under uniaxial tension

A method for calculating the stress-strain state of layers of cross-ply laminate based on an experimental deformation diagram under uniaxial tension is proposed. The essence of the method consists in solving a system of two equations describing the experimental curves σx = f(εx) and σx = f(εy), which allows determination of two unknown parameters related to the secant elastic characteristics of the material layers. The law of change in the remaining unknown parameters is set by assumptions regarding deformation of the polymer matrix composite and its layers during loading. To carry out the calculation, it is necessary to set the initial values of the elastic properties of the unidirectional material of the layers, which should be well consistent with the initial values of the elastic properties of the structure under study determined from the experiment. According to the developed algorithm, calculated dependences between average stresses, deformations and secant elastic properties of the layers of the structure are obtained (0°/90°/90°/0°) made of fiberglass E-Glass/MY750 using experimental data from the literature. Calculations carried out for three sets of initial values of the elastic properties of the material under study showed qualitatively identical results. The transverse tensile stress in the 90° layer reaches a maximum in the first half of the stress-strain diagram, and then decreases to zero. A similar stress in the 0° layer reaches a maximum at the failure point of the structure under study. It is revealed that the maximum calculated values of transverse stresses acting in layers 0° and 90° noticeably exceed the transverse tensile strength of the material specified in the literature. The longitudinal tensile stress in the 0° layer reaches a maximum at the failure point and corresponds to 95% of the value of the longitudinal tensile strength of the material. The longitudinal compressive stress in the 90° layer is at a low level throughout the deformation process of the structure under study. The results of this study can be recommended for developing models of the behavior of layers with cracks in the matrix when loading a polymer matrix composite.

Generic characterization method for nano-gratings using deep-neural-network-assisted ellipsometry

Abstract As a non-destructive and rapid technique, optical scatterometry has gained widespread use in the measurement of film thickness and optical constants. The recent advances in deep learning have presented new and powerful approaches to the resolution of inverse scattering problems. However, the application of deep-neural-network-assisted optical scatterometry for nanostructures still faces significant challenges, including poor stability, limited functionalities, and high equipment requirements. In this paper, a novel characterization method is proposed, which employs deep-neural-network-assisted ellipsometry to address these challenges. The method processes ellipsometric angles, which are measured by basic ellipsometers, as functional signals. A comprehensive model is developed to profile nano-gratings fabricated by diverse techniques, by incorporating rounded corners, residual layers, and optical constants into an existing model. The stability of the model is enhanced by implementing several measures, including multiple sets of initial values and azimuth-resolved measurements. A simple compensation algorithm is also introduced to improve accuracy without compromising efficiency. Experimental results demonstrate that the proposed method can rapidly and accurately characterize nano-gratings fabricated by various methods, with relative errors of both geometric and optical parameters well controlled under 5 %. Thus, the method holds great promise to serve as an alternative to conventional characterization techniques for in-situ measurement.

Chaos in a tunneling universe

Abstract A recent quasiclassical description of a tunneling universe model is shown to exhibit chaotic dynamics by an analysis of fractal dimensions in the plane of initial values. This result relies on non-adiabatic features of the quantum dynamics, captured by new quasiclassical methods. Chaotic dynamics in the early universe, described by such models, implies that a larger set of initial values of an expanding branch can be probed.

New stochastic convergence theorems: Overcoming the limitations of LaSalle theorems

LaSalle theorems, sometimes called as invariance-like theorems, have witnessed abundant applications as powerful tools of analysing system convergence. However, these theorems have twofold limitations: On the one hand, the uniformity of convergence with respect to initial state values cannot be offered, which indicates that the possibility of excessively slow convergence could not be ruled out for a given bounded set of initial state values. On the other hand, unbounded time-variations are not allowed in the systems, and meanwhile, the boundedness of all the states are required even if one merely concerns the convergence of partial states, precluding many scenarios with the unboundedness (e.g., induced by stochastic noise) for some states defined on the whole time horizon. Towards the limitations of LaSalle theorems, this paper seeks to further develop convergence theorems in the stochastic framework. First, a Lyapunov-like function based theorem on the convergence owning the uniformity with respect to initial state values is presented for Itô-type stochastic systems, with the infinitesimal of Lyapunov-like functions exploited more delicately than those in stochastic LaSalle theorems. Based on this, a framework of adaptive stabilization with the uniformity of convergence is established for uncertain stochastic nonlinear systems. In particular, the performance specifications involved are impossible to achieve by applying LaSalle theorems as in the related results. Second, an enlarged convergence theorem is established with Lyapunov-like conditions moderately relaxed, which allows the unboundedness for partial states defined on the whole time horizon and has potential applications in the presence of unbounded time-variations. What is notable, the enlarged convergence theorem is nontrivial to verify, which compels us to propose an extended Barbălat lemma with the conventional requirement of uniform continuity relaxed in the stochastic framework.

Quantum approximate optimization algorithm parameter prediction using a convolutional neural network

The Quantum approximate optimization algorithm (QAOA) is a quantum-classical hybrid algorithm aiming to produce approximate solutions for combinatorial optimization problems. In the QAOA, the quantum part prepares a quantum parameterized state that encodes the solution, where the parameters are optimized by a classical optimizer. However, it is difficult to find optimal parameters when the quantum circuit becomes deeper. Hence, there is numerous active research on the performance and the optimization cost of QAOA. In this work, we build a convolutional neural network to predict parameters of depth p + 1 QAOA instance by the parameters from the depth p QAOA counterpart. We propose two strategies based on this model. First, we recurrently apply the model to generate a set of initial values for a certain depth QAOA. It successfully initiates depth 10 QAOA instances, whereas each model is only trained with the parameters from depths less than 6. Second, the model is applied repetitively until the maximum expected value is reached. An average approximation ratio of 0.9759 for Max-Cut over 264 Erdős–Rényi graphs is obtained, while the optimizer is only adopted for generating the first input of the model.

Practical issues in data-driven model-free adaptive control for an omnidirectional mobile manipulator

This article focuses on addressing three practical issues encountered when applying a data-driven model-free adaptive control (MFAC) approach to mobile robots. The first practical issue lies in a common assumption in MFAC schemes that the sign of all elements in pseudo-partial derivative (PPD) should be constant, while it cannot be satisfied if omnidirectional mobile manipulators (OMMs) move with platform rotation. To solve this problem, a new coordinate frame is introduced, which is crucial for applying MFAC to any mobile robots with rotation. The second one is that the initial value setting method for estimation of PPD is unclear. Improper settings may easily lead to control system instability. An initial value setting method for estimation of PPD is proposed with explicit physical interpretation. Lastly, applying the typical MFAC scheme directly to OMM fails to converge well to the desired trajectory. To tackle this, a new data-driven MFAC controller is proposed by incorporating a sliding mode control. Finally, experimental tests on an OMM are carried out to verify the effectiveness of the proposed control scheme. To the best of our knowledge, this is the first MFAC scheme that has been experimentally verified on a prototype mobile robot with rotation.

Adolescent Dance Team Fitness: Initial Quantitative Normative Values.

Research on improving dancer fitness to enhance performance capacity and prevent injury continues to grow. Fitness research for the adolescent dance team population, however, presents an evidence void. Utilizing studio-based assessments, this descriptive quantitative study reveals an initial set of fitness normative values for the female adolescent competitive dance team dancer population. 115 female dance team dancers ages 12 to 17 participated in a 90-minute field test assessment battery for cardiorespiratory fitness, muscle endurance of the upper body, lower body, and core, and lower extremity power. Descriptive statistics (mean, standard deviation, and interquartile range) created an initial set of quantitative fitness normative values for this population. Dancers' results revealed 29.5mL/O2×min ± 5.7 estimated VO2max, 35 ± 16 push ups, 62 ± 14 half sit ups, 51 ± 13 squats in 1 minute, 190 ± 23 cm broad jump, 445 ± 65 cm right lower extremity triple hop, and 450 ± 69 cm/left lower extremity triple hop. Discussion includes comparison of findings relative to previously established adolescent fitness and dancer normative data. Discussion also suggests implications, study limitations, and future research directions.

A novel variational Bayesian adaptive Kalman filter with mismatched process noise covariance matrix

AbstractThis paper proposes a novel variational Bayesian (VB) adaptive Kalman filter with mismatched process noise covariance matrix (PNCM). Firstly, this paper explains the reason why the predicted error covariance matrix (PECM) is chosen for variational inference. Secondly, compared with the earlier VB adaptive Kalman filter (VB‐AKF‐Q), the proposed filter calculate the dynamic model of the PECM with its historical estimation information. Therefore, the proposed filter can overcome the influence of mismatched PNCM on the initial value setting of PECM in the VB‐AKF‐Q. Finally, we use the evidence lower bound for the proposed filter and give the convergence criterion on this basis. Some examples with a target tracking simulation are carried out to demonstrate the superiority of the proposed filter.

Variations in ERP data quality across paradigms, participants, and scoring procedures.

Although it is widely accepted that data quality for event-related potential (ERP) components varies considerably across studies and across participants within a study, ERP data quality has not received much systematic analysis. The present study used a recently developed metric of ERP data quality- the standardized measurement error (SME)-to examine how data quality varies across different ERP paradigms, across individual participants, and across different procedures for quantifying amplitude and latency values. The EEG recordings were taken from the ERP CORE, which includes data from 40 neurotypical college students for seven widely studied ERP components: P3b, N170, mismatch negativity, N400, error-related negativity, N2pc, and lateralized readiness potential. Large differences in data quality were observed across the different ERP components, and very large differences in data quality were observed across participants. Data quality also varied depending on the algorithm used to quantify the amplitude and especially the latency of a given ERP component. These results provide an initial set of benchmark values that can be used for comparison with previous and future ERP studies. They also provide useful information for predicting effect sizes and statistical power in future studies, even with different numbers of trials. More broadly, this study provides a general approach that could be used to determine which specific experimental designs, data collection procedures, and data processing algorithms lead to the best data quality.

Study on crack identification for pipe with through-wall crack by cuckoo optimization algorithm

In recent years, the aging of power plants and other infrastructure facilities has become an issue. Therefore, a health-monitoring technology has attracted attention in order to ensure the integrity of structures. An inverse analysis is conducted to estimate the size and position of cracks in this technology. The accuracy of the inverse analysis is dependent on the form of the objective function and the optimization calculation method. Generally, optimization calculation methods using the gradient of the objective function is adopted. However, it is known to have the drawback that the search solution strongly depends on the initial value setting. The biomimetics-based cuckoo optimization algorithm (COA) is a method that can overcome this drawback. However, there are few examples of estimating the crack size and position in structures using the COA. In this study, an inverse analysis method is developed to estimate the crack angle and position of circumferential through-wall crack in pipes. In this study, transfer matrix method (TMM) was utilized to numerically calculate the deformation and rotation angle of the pipe, which are required in the inverse analysis. For this purpose, the fundamental theory of the TMM was extended to the element with a through-wall crack. Consequently, it was confirmed that the crack angle, position and magnitude of the applied shear load can be accurately estimated by the developed method.

Sustainable logistics hubs: greenhouse gas emissions as one sustainability key performance indicator

Quantifying greenhouse gas (GHG) emissions of logistics hubs such as warehouses, transshipment sites or terminals enables understanding the use of resources and improving the efficiency of operations. Thus, it supports decision-makers on their roadmap to climate neutrality and within the transformation towards sustainable logistics hubs. This research provides an overview of relevant indicators that can be used for measuring the sustainability performance of hubs, along the full life cycle from construction, operation, through to renovation and end-of-life management. Current approaches for the quantification of embodied and operational carbon are summarized. Results of the international market study on energy efficiency and GHG emissions at hubs are outlined and an initial set of GHG emission indicator values for European hubs provided.

A uniformisation-driven algorithm for inference-related estimation of a phase-type ageing model.

We develop an efficient algorithm to compute the likelihood of the phase-type ageing model. The proposed algorithm uses the uniformisation method to stabilise the numerical calculation. It also utilises a vectorised formula to only calculate the necessary elements of the probability distribution. Our algorithm, with an error's upper bound, could be adjusted easily to tackle the likelihood calculation of the Coxian models. Furthermore, we compare the speed and the accuracy of the proposed algorithm with those of the traditional method using the matrix exponential. Our algorithm is faster and more accurate than the traditional method in calculating the likelihood. Based on our experiments, we recommend using 20 sets of randomly-generated initial values for the optimisation to get a reliable estimate for which the evaluated likelihood is close to the maximum likelihood.

Информационные технологии на основе шумоподобных сигналов: III. Cпектр периодов последовательностей, формируемых дискретным хаотическим алгоритмом

The patterns of formation of pseudo-random sequences by discrete chaotic algorithms with a delay of the Fibonacci type are studied. The algorithms are defined on a closed interval of integers; the generated numbers are returned to a given interval, which provides an effective mechanism for mixing in the phase space. For various values of the parameters, the spectra of periods of sequences are determined for an arbitrary set of initial values that uniquely determine the state of the system in its phase space. Relationships are obtained that make it possible to reduce the search time for the maximum period of a pseudo-random sequence formed by a recurrent chaotic Fibonacci-type algorithm.

A Motion Control Simulation Based on Predictive Model

How to improve target tracking efficiency, how to track targets with different motion forms, and establish target motion models are the main research directions. This paper expounds a motion control algorithm based on the prediction model. By establishing the motion equation and prediction model of the target, setting the estimated parameters, optimizing the initial value setting, and predicting the motion state of the target, the simulation analysis based on MATLAB is completed. The simulation results show that the motion control algorithm based on the prediction model has good robustness and real-time performance, realizes the feedback control closed-loop of “prediction-error-correction-prediction”, and can effectively reduce the real-time calculation amount of target tracking. Good target tracking accuracy.

Stochastic Parareal: An Application of Probabilistic Methods to Time-Parallelization

Parareal is a well-studied algorithm for numerically integrating systems of time-dependent differential equations by parallelising the temporal domain. Given approximate initial values at each temporal sub-interval, the algorithm locates a solution in a fixed number of iterations using a predictor-corrector, stopping once a tolerance is met. This iterative process combines solutions located by inexpensive (coarse resolution) and expensive (fine resolution) numerical integrators. In this paper, we introduce a stochastic parareal algorithm aimed at accelerating the convergence of the deterministic parareal algorithm. Instead of providing the predictor-corrector with a deterministically located set of initial values, the stochastic algorithm samples initial values from dynamically varying probability distributions in each temporal sub-interval. All samples are then propagated in parallel using the expensive integrator. The set of sampled initial values yielding the most continuous (smoothest) trajectory across consecutive sub-intervals are fed into the predictor-corrector, converging in fewer iterations than the deterministic algorithm with a given probability. The performance of the stochastic algorithm, implemented using various probability distributions, is illustrated on low-dimensional systems of ordinary differential equations (ODEs). We provide numerical evidence that when the number of sampled initial values is large enough, stochastic parareal converges almost certainly in fewer iterations than the deterministic algorithm, maintaining solution accuracy. Given its stochastic nature, we also highlight that multiple simulations of stochastic parareal return a distribution of solutions that can represent a measure of uncertainty over the ODE solution.

Effect of Color Temperature and Illuminance on Psychology, Physiology, and Productivity: An Experimental Study

In this study, we investigated the impact of the lighting environment on psychological perception, physiology, and productivity and then designed lighting control strategies based on the experimental results. The research was conducted in a smart lighting laboratory, and 67 subjects were tested in different illuminances and correlated color temperatures (CCTs). During the experiment, the physiological data of subjects were continuously recorded, while the psychology and productivity results were evaluated by questionnaires and working tests, respectively. The experimental results found that both illuminance and CCT could significantly influence the feeling of comfort and relaxation of the subjects. Warm CCT and higher illuminance (3000 K–590 lux) made subjects feel more comfortable. Productivity reached its maximum value with illuminance above 500 lux and equivalent melanopic lux (EML) higher than 150. The brain-wave and heart-rate changes did not have a close relationship with either illuminance or CCT, but the heart rate slightly increased in the adjustable lighting mode. Regardless of the initial value setting, the subjects preferred intermediate CCT (4200 K) and bright illumination (500 lux) after self-adjustment. Finally, we proposed three comprehensive lighting control strategies based on psychology, productivity, circadian rhythm, and energy-saving.

Image analysis for patterns in solutions of reaction–diffusion equations

We consider reaction–diffusion equations in form of a Turing system, i.e., a system of partial differential equations (PDEs) in time and two-dimensional space. The equations model the formation of patterns in animal coats and skins, for example. Stationary solutions represent a final pattern, which depends both on physical parameters and initial values. A spatial finite difference method yields a high-dimensional system of ordinary differential equations (ODEs). Stationary solutions of the ODEs are computed numerically to approximate steady-state solutions of the PDEs. We investigate properties of the patterns using image analysis. The two-dimensional stationary solutions are converted into binary images. This approach allows for the application of methods and algorithms on the binary image. The Euler characteristic yields the number of objects in this case. Properties of each object like area, Feret diameters, circularity, etc. can be computed. Moreover, we examine associated statistical quantities for sample sets of initial values in a Monte-Carlo simulation. Our aim is to analyse the dependence as well as the sensitivity of patterns on the physical parameters. We present results of numerical computations for different selections of those parameters.

Quasi-zero rigidity condition for static force characteristic of parallelogram mechanism for seat vibration protection system

Introduction. Reducing vibration effects on machine operators is an urgent task; it allows to minimize the impact of vibrations on health, increase efficiency and attention. Accuracy and productivity can be improved. The problem of vibration protection is relevant for all types of machines, the working bodies of which interact with the soil and road surface. One of the impact reduction directions, along with vibration protection of cabs, is the development of vibration protection systems for operator seats. It is promising to use the effect of quasi-zero rigidity, which makes it possible to effectively damp low-frequency oscillations. For the proposed design based on a parallelogram mechanism, it is necessary to determine the condition for the horizontalness of the middle section of the static power characteristic.Materials and Methods. For the developed design scheme, using well-known power static and geometric relationships, novel analytical relationships between the parameters of the initial data and the vertical lifting force of the mechanism were derived. The initial parameters include the linear dimensions of the mechanism, the size of the quasi-zero rigidity zone, the weight of the chair with the operator, and the spring stiffness. When deriving analytical dependencies, the coordinates of the points of the mechanism moving and stationary relative to their own base were used.Results. The use of the developed analytical dependencies allows, for the given parameters of the initial data, to build a static power characteristic of the mechanism. The middle section of the characteristic is close to horizontal, but in general it is not horizontal. As an example, a static characteristic is given for a set of initial data values, the middle part of which is not horizontal. Equating the values of the vertical force of the mechanism at the left and right boundary points of the middle part of the characteristic made it possible to determine analytical dependences for ensuring quasi-zero rigidity.Discussion and conclusions. The obtained analytical expressions for ensuring the horizontalness of the middle section of the static power characteristic were verified. Applying the derived conditions decreases the number of parameters in the original data by one. An increase in the two dimensional parameters of the mechanism significantly reduces the required rigidity of the mechanism spring, which reduces the metal consumption.

Research on a Real-Time Estimation Method of Vehicle Sideslip Angle Based on EKF.

In this article, a real-time vehicle sideslip angle state observer is proposed, which is based on the EKF algorithm. Firstly, based on a 2-DOF dynamical model and the tire lateral force model, the vehicle sideslip angle state observer model with a self-adapted truncation procedure is established by combining the EKF and the least squares methods. The calculation of the Jacobi matrix in the time domain is transformed into a calculation in the frequency domain. A self-adapted update noise estimation method and an initial value setting strategy are proposed as well. Finally, a hardware-in-the-loop simulation is carried out by Matlab/Simulink-CarSim-dSPACE, and the real-time reliability of the estimation method is verified and analyzed by RMSE.

Towards a new methodology for the characterisation of crack tip fields based on a hybrid computational approach

This work presents a hybrid optimisation technique for the simultaneous calculation of crack tip characterising parameters and its spatial location, which can significantly affect the characterising parameters if the position used is inaccurate. The hybrid technique combines initial use of a genetic algorithm to obtain a well-conditioned set of initial parameter values that is then passed to an interior point optimisation algorithm for subsequent fast optimisation. Use of the hybrid technique is also amenable to easy automation. The capability of the technique is demonstrated using the CJP crack tip field model, with digital image correlation (DIC) being used to measure the 2D crack tip displacement field. This model was chosen, not only for its demonstrated sensitivity to accuracy in crack tip location, but also for its proven utility in providing effective crack growth correlation in the presence of plasticity-induced shielding across a wide range of growth rates. The results obtained from the hybrid technique are shown to be reliable through comparison with results obtained using other established techniques.