Initial State Parameters Research Articles

Cone penetration tests in saturated and unsaturated silty tailings

Laboratory-controlled cone penetration test results for two silty tailings in a variety of saturated and unsaturated states, obtained using two calibration chambers, are presented then interpreted using a state parameter-based approach. For each, the cone penetration resistances, which increase due to the presence of suction when the tailings are unsaturated, can be normalised using the initial mean effective stress to establish a relationship with the initial state parameter. The relationship is applicable to saturated and unsaturated conditions, as long as the presence of suction hardening as well as the influence of suction on the mean effective stress are accounted for, and as long as the cone penetrations occur under drained conditions. The relationships enable state parameters to be back-calculated from normalised cone penetration resistances. The state parameters enable estimations of the tailings’ peak friction angles for drained loadings as well as their propensities to liquefy during undrained loadings. Application is demonstrated using cone penetration test soundings in the tailings storages from which the samples were taken, showing how in situ void ratios and state parameters, as well as future state parameters if the tailings were to become saturated, can be determined. Close agreements with direct measurements of void ratios are shown.

A Compressed Timeline Approach to Residential Corridor Smoke Control Modelling Using Initial Apartment Conditions

It is common for fire engineers in the UK to consider a time-dependent series of events when using the Fire Dynamics Simulator to assess the performance of residential corridor smoke control systems. A significant proportion of this established timeline focusses on producing the necessary buildup of smoke and heat in the apartment of origin, prior to any smoke spreading to the corridor or stairs. This paper proposes an alternative approach that enables a compressed modelling timeline by using uniform initial condition parameters in the apartment of origin for the soot mass fraction, room gas temperature, wall and ceiling temperatures, and slab temperature. These conditions can be applied from the outset for separate means of escape and firefighting phases, providing greater flexibility and reducing the wall-clock times for simulations. To achieve this, simulations for a 1000 kW medium growth rate fire in a single reasonable worst-case exemplar arrangement were undertaken using an established timeline approach. This was then used to estimate the approximate initial conditions needed to inform the compressed timeline input parameters. When comparing the established and compressed timeline approaches, close agreement was demonstrated for the estimated conditions of corridor visibility and temperature. The paper also suggests prospective correlations for the initial condition parameters needed to represent a given maximum heat release rate. The paper ultimately provides engineers with a method for residential corridor smoke control modelling that reduces simulation times, as well as providing greater consistency in the demonstrated performance, by proposing initial conditions independent of the dimensions of the apartment.

Mixed uncertainty quantification for terminal flight state of Mars atmospheric entry

China's Tianwen-1 successfully touched down on the surface of the red planet, even though various mixed uncertainties occurred in flight dynamics during Mars atmospheric entry, especially Gaussian mixed uniform uncertainty in the initial state and dynamics parameters. A hybrid algorithm combining sensitivity collocation with nonintrusive polynomial chaos was proposed in this paper. Mixed uncertainty was quantified to reveal the mechanism causing location deviation during parachute deployment from the preplanned point. First, Mars atmospheric entry dynamics containing uncertainty were modeled as stochastic nonlinear dynamics. Second, stochastic variables were approximated using nonintrusive polynomial chaos, and sensitivity collocation was used to characterize state variables under the influence of uncertainty. Finally, the probabilistic distribution of the parachute deployment state was analytically obtained according to the initial and parametric uncertainties at the entry interface. Comparison simulations verified the effectiveness of the proposed technique. Gaussian-uniform mixed uncertainty features from the entry interface to the beginning of parachute deployment were revealed.

Efficient Colour Image Encryption Algorithm Using a New Fractional-Order Memcapacitive Hyperchaotic System

In comparison with integer-order chaotic systems, fractional-order chaotic systems exhibit more complex dynamics. In recent years, research into fractional chaotic systems for the utilization of image cryptosystems has become increasingly highlighted. This paper describes the development, testing, numerical analysis, and electronic realization of a fractional-order memcapacitor. Then, a new four-dimensional (4D) fractional-order memcapacitive hyperchaotic system is suggested based on this memcapacitor. Analytically and numerically, the nonlinear dynamic properties of the hyperchaotic system have been explored, where various methods, including equilibrium points, phase portraits of chaotic attractors, bifurcation diagrams, and the Lyapunov exponent, are considered to demonstrate the chaos behaviour of this new hyperchaotic system. Consequently, an encryption cryptosystem algorithm is used for colour image encryption based on the chaotic behaviour of the memcapacitive model, where every pixel value of the original image is incorporated in the secret key to strengthen the encryption algorithm pirate anti-attack robustness. For generating the keyspace of that employed cryptosystem, the initial condition values, parameters, and fractional-order derivative value(s) (q) of the memcapacitive chaotic system are utilized. The common cryptanalysis metrics are verified in detail by histogram, keyspace, key sensitivity, correlation coefficient values, entropy, time efficiency, and comparisons with other recent related fieldwork in order to demonstrate the security level of the proposed cryptosystem approach. Finally, images of various sizes were encrypted and recovered to ensure that the utilized cryptosystem approach is capable of encrypting/decrypting images of various sizes. The obtained experimental results and security metrics analyses illustrate the excellent accuracy, high security, and perfect time efficiency of the utilized cryptosystem, which is highly resistant to various forms of pirate attacks.

Hammerstein-Wiener nonlinear system identification by using honey badger algorithm hybridized Sage-Husa adaptive Kalman filter with real-time applications

This paper introduces a hybrid Sage-Husa adaptive Kalman filter (SHAKF) with a metaheuristic algorithm to estimate the parameters of the Hammerstein-Wiener (H-W) model. The initial state parameters of the SHAKF must be appropriately tuned based on the estimation problem; otherwise, it would lead to divergence. To get the global optimal tuned SHAKF parameters for modelling the H-W system, this proposed work has formulated an objective function based on the identification problem optimised with a recently introduced metaheuristic technique called the Honey Badger algorithm (HBA). Finally, the unknown parameters of the H-W system are identified by using the basic SHAKF technique by considering the achieved optimised SHAKF parameters as the initial state inputs. The efficiency of the proposed technique is verified on numerical problems, and its practical feasibility is tested on mechanical systems, namely arm robots and ball-and-beam system identification based on real data sets. The experimental results warrant that the proposed technique possesses high robustness to additive noise, quick convergence speed and low steady-state error compared to the other recently reported methods. For comparison study, different metaheuristic algorithms, namely, comprehensive learning particle swarm optimiser (CLPSO), an ensemble of mutation strategies and control parameters with the differential evolution (EPSDE) and colliding bodies optimisation (CBO), are incorporated into SHAKF to estimate the H-W systems and the obtained results for various standard metrics show the superiority of the proposed HBA-aided SHAKF method over others.

Sampling effects in quantum mechanical/molecular mechanics trajectory surface hopping non-adiabatic dynamics.

The impact of different initial conditions in non-adiabatic trajectory surface hopping dynamics within a hybrid quantum mechanical/molecular mechanics scheme is investigated. The influence of a quantum sampling, based on a Wigner distribution, a fully thermal sampling, based on classical molecular dynamics, and a quantum sampled system, but thermally equilibrated with the environment, is investigated on the relaxation dynamics of solvated fulvene after light irradiation. We find that the decay from the first singlet excited state to the ground state shows high dependency on the initial condition and simulation parameters. The three sampling methods lead to different distributions of initial geometries and momenta, which then affect the fate of the excited state dynamics. We evaluated both the effect of sampling geometries and momenta, analysing how the ultrafast decay of fulvene changes accordingly. The results are expected to be of interest to decide how to initialize non-adiabatic dynamics in the presence of the environment.This article is part of the theme issue ‘Chemistry without the Born–Oppenheimer approximation’.

Incremental integral reconstitution for detecting initial condition effects

The five-dimensional (5-D) two-memristor-based jerk (TMJ) system can emerge the initial condition-dependent extreme multi-stability due to two zero roots caused by its plane equilibrium set. However, using conventional stability analysis methods, we cannot explain the kinetic mechanism of initial condition effects on the 5-D TMJ system. To this end, this paper establishes a three-dimensional (3-D) incremental integral reconstructed (IIR) model using an integral transformation method, and converts the internal initial conditions and plane equilibrium set of the original system into the explicit initial condition parameters (ICPs) and one or no equilibrium point of the reconstructed model respectively. With the explicit ICPs and determined equilibrium point of the reconstructed model, the memristor and non-memristor initial condition effects on the original system are thereby detected by the stability distributions and then confirmed by the kinetic distributions. The results show that the memristor and non-memristor initial conditions have powerful influence on the kinetic effects of the 5-D TMJ system, and its kinetic mechanism of initial condition effects can be explained effectively based on the 3-D IIR model. Besides, with analog circuit and digital circuit platform, the circuit simulations and hardware experiments verify the kinetic effects of the ICPs on the 3-D IIR model.

Detection and Tracking of Moving Object-Using Kalman Filter Enhancement (KF) by Grasshopper Optimization Algorithm

Today, the process of tracking moving objects in Video sequence has many real applications such as robots' systems, surveillance systems, and monitoring systems, visual information processing, and so more. The first stage in tracking moving object systems is detection the target in Video sequence and images and the second stage is to track the identified object. In this regard, Automatic detection and tracking of object is an attractive scope in now researches, so the proposed method based on the detection-tracking target moving objects by using the Kalman Improved Filter (KF) method. The Kalman filter, assuming initial state and noise covariance parameter for detection the object, which are critical parameters for estimating speed. For successful tracking by Kalman filter, the noise covariance matrix must be optimized. Therefore, in many studies, different methods based on optimization and metaheuristic algorithms have been proposed to increase the performance of Kalman filter method. In this research to adjust, the noise covariance is of the Kalman filter for object tracking and improve the initial parameters of it, using grasshopper optimization algorithm (GOA). Here considered not only the properties of the object, but also the estimation of the motion of the object to speed up the search process. To compare the new method's efficiency and accuracy, we used MATLAB R2019b software. The results show that the proposed method has improved at least 10% in accuracy compared to RMOT and CMOT and 20% in recovery compared to Recall. In addition, the proposed method has at least a 2% improvement in Recall parameters and accuracy compared to the evaluated article.

Cryptanalysis of a Hamming Code and Logistic-Map based Pixel-Level Active Forgery Detection Scheme

In this paper, we analyze the security of a fragile watermarking scheme for tamper detection in images recently proposed by S. Prasad et al. The chaotic functions are used in the scheme to exploit its pseudo-random behavior and its sensibility to initial condition and control parameter, but despite that, security flaws have been spotted and cryptanalysis of the scheme is conducted. Experimental results shows that the scheme could not withstand the attack and watermarked images were manipulated without triggering any alarm in the extraction scheme. In this paper, two different approaches of attacks are demonstrated and conducted to break the scheme. This work falls into the context of improving the quality of the designed cryptographic schemes taking into account several cryptanalysis techniques.

Thermal coherence of Heisenberg model with dipole interaction and magnetic external field

The dynamics of coherence in a two-qubit Heisenberg XXX model with magnetic field in the presence of the intrinsic decoherence is investigated. We discuss the influence of dipole parameter D, spin distance r, magnetic field Bz and initial state parameter α on coherence of the long time limit situation. Then, we discuss how decoherence changes with time with the initial state parameter α and the decoherence parameter γ.

A Hollow Cylinder Torsional Shear Device to Explore Behavior of Soils Subjected to Complex Rotation of Principal Stresses

Abstract A hollow cylinder torsional shear device that is modified to simulate more generalized earthquake loading, involving simultaneous compression and shear wave loading in situ, is presented. The nature and degree of stress rotation due to coupled action of compression waves and shear waves are discussed, and illustrative experiments conducted to simulate different loading scenarios in the laboratory are presented. The stress rotation due to this coupled loading is significantly influenced by the initial consolidation stress state and shearing parameters such as the ratio between shear stress and normal stress increments (ΔS/ΔN) and the phase shift (ϕ) between the waves. The representative (ΔS/ΔN) ratios were obtained from the numerical simulations and used in hollow cylinder torsional shear apparatus to investigate the cyclic response of Fraser River sand under the simultaneous action of normal and shear stresses. Typical cyclic test results demonstrating the capability of a hollow cylinder torsional shear apparatus commissioned at Carleton University in following complicated cyclic loading paths are presented in this paper. It is noted that loading under such nonconventional stress paths, such as elliptical and circular paths, could be initiated along different pathways. Cyclic test results along these pathways demonstrate that soil response is dependent on the overall path including the initial stress state. Test results also highlighted the significance of stress rotation and cyclic stress paths in affecting the liquefaction susceptibility of sands.

Construction of analytical solution for Hirota–Satsuma coupled KdV equation according to time via new approach: Residual power series

In this work, a modern and novel approach method called the residual power series technique has been applied to find an analytical solution for an important equation in optical fibers called the Hirota–Satsuma coupled KdV equation with time as a series solution. Comparison of the analytical approximate solution with the exact solution concluded that the present method is an important addition for analyzing a system of partial differential equations that have a strong nonlinear term. We also represented graphically and discussed the effect of initial condition parameters and reaction of time on the model.

Reversible optical–microwave quantum conversion assisted by optomechanical dynamically dark modes

We propose a dynamically dark-mode (DDM) scheme to realize the reversible quantum conversion between microwave and optical photons in an electro-optomechanical (EOM) model. It is shown that two DDMs appear at certain times during the dynamical evolution of the EOM model. It is demonstrated that the DDMs can induce two kinds of reversible and highly efficient quantum conversion between the microwave and optical fields, the conditional quantum conversion (CQC) and the entanglement-assisted quantum conversion (EAQC). The CQC happens at the condition of vanishing of the initial-state mean value of one of the microwave and optical fields and only depends on the coupling ratio of the system under consideration. The EAQC occurs in the presence of the initial-state entanglement between the microwave and optical fields. It is found that the EAQC can be manipulated by engineering the initial-state entanglement and the coupling ratio. It is indicated that it is possible to realize the entanglement-enhanced (or suppressed) quantum conversion through controlling the phase of the initial-state parameter. Our work highlights the power of generating reversible and highly efficient quantum conversion between microwave and optical photons by the DDMs.

Comparison of implicit and explicit numerical integration schemes for a bounding surface soil model without elastic range

Standard integration schemes for rate constitutive equations were designed within the classical theory of plasticity. Consequently, they rely on the assumption that a yield criterion defines a range of purely elastic material behaviour. Many constitutive models for non-cohesive soils discard that assumption. They account for inelastic deformations without employing a yield criterion. This simplifies the formulation of the models but raises questions concerning their numerical implementation. In particular, it is not fully clear which algorithmic method is most appropriate for the numerical integration of constitutive models without elastic range. To investigate this, two different stress point algorithms for a critical state bounding surface model for sands were developed and implemented. The explicit method employs substepping to automatically control the local error. The implicit method updates stresses and state variables through a local Newton iteration, the Jacobian of which is computed by numerical differentiation. The two algorithms were compared by means of calculations at integration point level as well as with respect to a boundary value problem. The results show that for a given level of accuracy, the explicit update procedure is significantly more efficient than the implicit one. This holds regardless of initial state parameters and input strain increment magnitudes.

Neural UpFlow

We present a novel up-resing technique for generating high-resolution liquids based on scene flow estimation using deep neural networks. Our approach infers and synthesizes small- and large-scale details solely from a low-resolution particle-based liquid simulation. The proposed network leverages neighborhood contributions to encode inherent liquid properties throughout convolutions. We also propose a particle-based approach to interpolate between liquids generated from varying simulation discretizations using a state-of-the-art bidirectional optical flow solver method for fluids in addition with a novel key-event topological alignment constraint. In conjunction with the neighborhood contributions, our loss formulation allows the inference model throughout epochs to reward important differences in regard to significant gaps in simulation discretizations. Even when applied in an untested simulation setup, our approach is able to generate plausible high-resolution details. Using this interpolation approach and the predicted displacements, our approach combines the input liquid properties with the predicted motion to infer semi-Lagrangian advection. We furthermore showcase how the proposed interpolation approach can facilitate generating large simulation datasets with a subset of initial condition parameters.

Research on intelligent mobile robot full coverage path planning

Aiming at the full coverage path planning problem of intelligent robot, a local chaos evaluation planning method is proposed. Considering the randomness and unpredictability of the mobile robot, a four-dimensional chaotic system with feedback control variables is designed. The path planning model is established by fusing the system with the robot motion model. At the same time, the coupling control parameters are introduced to adjust the system error. According to the initial coordinates and chaotic initial state parameters of the robot, the moving path points can be calculated by differential discretization. Considering the local optimal solution of the path planning, the mobile space of the robot is meshed, and the dynamic mesh activity value is calculated according to the excitation. The mesh activity is used to divide the mobile path planning, and then the local path and angular displacement changes are obtained. At the same time, the corresponding index evaluation is designed for local path planning to correct the planning results. The simulation results show that the proposed local chaos evaluation planning method has a good effect of full path coverage, and obtains lower path repetition rate, moving distance and path planning time, which effectively improves the mobile efficiency and control stability of the robot.

Performance of the Unified Readout System of Belle II

The Belle II experiment at the SuperKEKB collider at KEK, Tsukuba, Japan has successfully started taking data with the full detector in March 2019. Belle II is a luminosity frontier experiment of the new generation to search for physics beyond the Standard Model of elementary particles, from precision measurements of a huge number of B and charm mesons and tau leptons. In order to read out the events at a high rate from the seven subdetectors of Belle II, we adopt a highly unified readout system, including a unified trigger timing distribution system (TTD), a unified high speed data link system (Belle2link), and a common backend system to receive Belle2link data. Each subdetector frontend readout system has a field-programmable gate array (FPGA) in which unified firmware components of the TTD receiver and Belle2link transmitter are embedded. The system is designed for data taking at a trigger rate up to 30 kHz with a dead-time fraction of about 1% in the frontend readout system. The trigger rate is still much lower than our design. However, the background level is already high due to the initial vacuum condition and other accelerator parameters, and it is the most limiting factor of the accelerator and detector operation. Hence the occupancy and radiation effects to the frontend electronics are rather severe, and they cause various kind of instabilities. We present the performance of the system, including the achieved trigger rate, dead-time fraction, stability, and discuss the experience gained during the operation.

Estimation of solid-state sintering and material parameters using phase-field modeling and ensemble four-dimensional variational method

Sintering is a fundamental technology for powder metallurgy, the ceramics industry, and additive manufacturing processes such as three-dimensional printing. Improvement of the properties of sintered materials requires prediction of their microstructure using numerical simulations. However, the physical values and material parameters used for such predictions are generally unknown. Data assimilation (DA) enables the estimation of unobserved states and unknown material parameters by integrating simulation results and observational data. In this paper, we develop a new model that couples an ensemble-based four-dimensional variational (En4DVar) DA with a phase-field model of solid-state sintering (En4DVar-PF model) to estimate the state of the sintered material and multiple unknown material parameters. The developed En4DVar-PF model is validated by numerical experiments called twin experiments, in which a priori assumed-true initial state and multiple material parameters are estimated. The results of the twin experiments demonstrate that, using only three-dimensional morphological data of the sintered microstructure, our developed En4DVar-PF model can simultaneously and accurately estimate the particle shape, distribution of grain boundaries, and material parameters, including diffusion coefficients and mobilities related to grain boundary migration. Furthermore, our work identifies criteria for determining appropriate DA conditions such as the observational time interval required to accurately estimate the material parameters using our developed model. The developed En4DVar-PF model provides a promising framework to obtain unobservable states and difficult-to-measure material parameters in sintering, which is crucial for the accurate prediction of sintering processes and for the development of superior materials.

MetaFlow: a meta-learning-based network for optical flow estimation

Convolutional neural networks (CNNs) have achieved success in optical flow estimation using labeled datasets, but they fail to build an internal representation to fast adapt to the specific task. On the other hand, limited by the lack of ground truth, existing CNNs-based methods suffer from high noise sensitivity and inferior generalization performance. We integrate the meta-learning technique with optical flow estimation, which can learn internal features to search optimal initial state parameters of the network. Meanwhile, we devise an enhanced network termed MetaFlow to further improve performance. MetaFlow extracts per-pixel features, builds correlation volumes for all pairs of pixels, and iteratively updates optical flow through optical flow predictor using meta-learning. In addition, we propose a meta-transfer pretraining approach to obtain initial network weights, which can efficiently avoid network overfitting. Empirical experiments on MPI Sintel and KITTI benchmarks have shown that the proposed MetaFlow achieves the state-of-the-art results and performs outstanding in challenging scenarios such as textureless regions and abrupt motions.

Nonlocality and coherence in double quantum dot systems

We examine the relationship among the fidelity, quantum coherence and Bell nonlocality in double quantum dot systems (DQDs) using transmission line resonator (TLR). We show how the physical quantities depend on the main parameters of the physical model and initial state parameter of the DQD. Moreover, we evaluate the total quantum correlation in DQDs using the skew information and display the time dependence of the quantum coherence and correlation on the main physical parameters.