Initial Problem Research Articles

A Novel Analytical Solution of Radial Diffusion Equation with Constant Terminal Rate of Slightly Compressible Fluid

In reservoir engineering, the flow and type of fluids crossing porous media, are associated with pressure drops leading to variations on hydrodynamic parameters and the filtration stages which are of interest to be assessed as they impact the production efficiency of oil and gas industry. These parameters are the key to the practical solutions of numerous subjects faced during oil field exploitation. Dealing with the problem of sound field development initiatives requires a rigorous examination of unsteady filtration of slightly compressible fluid in the reserves layer. The state of the art of the proposed scientific work is to present an innovative mathematical approach that gives unique results, in the mathematical connection and combination of the equations used versus existing diffusion equation in the case of the constant terminal rate solution. This model helps the designers in the field of oil and gas to better and faster evaluate the diffusivity of the pay zones, the different hydrodynamic parameters, and the different variables that take part in the development of fluid filtration processes in the porous medium expressed as in the dependence of time, distance, and other variables, all of which together impact the well testing and long-term projections.

SimCDL: A Simple Framework for Contrastive Dictionary Learning

In this paper, we propose a novel approach to the dictionary learning (DL) initialization problem, leveraging the SimCLR framework from deep learning in a self-supervised manner. Dictionary learning seeks to represent signals as sparse combinations of dictionary atoms, but effective initialization remains challenging. By applying contrastive learning, we encourage similar representations for augmented versions of the same sample while distinguishing between different samples. This results in a more diverse and incoherent set of atoms, which enhances the performance of DL applications in classification and anomaly detection tasks. Our experiments across several benchmark datasets demonstrate the effectiveness of our method for improving dictionary learning initialization and its subsequent impact on performance in various applications.

Understanding US adolescents’ and emerging adults’ overestimation of their cannabis use quantity

Cannabis is the most used federally illicit drug in the United States (U.S). Understanding how accurate adolescents and emerging adults are at estimating their cannabis use quantity is important, as this lays the groundwork for understanding and studying the outcomes associated with cannabis use. This study aimed to assess the accuracy of cannabis quantity in grams for a typical joint and identify predictors of accuracy among U.S. adolescents and emerging adults. Participants (n = 50; aged 14–21) who reported using joints in the past 30 days completed measures of cannabis use characteristics and problems, were asked to “eyeball” the amount of cannabis in their average joint using a cannabis substitute and estimated the number of grams. The cannabis substitute was then weighed in grams. Paired samples t-tests indicated that participants estimated significantly more grams in a joint (M=.82; SD = 0.46) than the actual amount of the surrogate substance (M=.47; SD = 0.21; t(49) = 6.32, p < 0.001, d = 0.89). A linear regression analysis indicated that the age of cannabis initiation (B = -0.13, p=.002) and cannabis use problems (B = -0.03, p=.009) were negatively and significantly associated with discrepancies in estimated grams. Specifically, being older when first using cannabis and reporting more cannabis use problems were linked to less overestimation of grams per joint. These findings align with previous research demonstrating a tendency to overestimate cannabis quantity and contribute novel insights by identifying predictors of estimation accuracy. This study underscores the importance of considering age and cannabis-related problems when assessing the reliability of self-reported cannabis use quantities among adolescents and emerging adults.

Initialization-free multistate memristor: Synergy of spin–orbit torque and magnetic fields

Spin–orbit torque (SOT)-based perpendicularly magnetized memory devices with multistate memory have garnered significant interest due to their applicability in low-power in-memory analog computing. However, current methods are hindered by initialization problems, such as prolonged writing duration, and limitations, on the number of magnetic states. Consequently, a universal method for achieving multistate in perpendicular magnetic anisotropy (PMA)-based stacks remains elusive. Here, we propose a general experimental method for achieving multistate without any initialization step in SOT-driven magnetization switching by integrating an external out-of-plane magnetic field. Motivated by macrospin calculations coupled with micromagnetic simulations, which demonstrate the plausibility of magnetization state changes due to out-of-plane field integration, we experimentally verify multistate behavior in Pt/Co/Pt and W/Pt/Co/AlOx stacks. The occurrence of multistate behavior is attributed to intermediate domain states with Néel domain walls. We achieve repeatable 18 multistate configurations with a minimal reduction in retentivity through energy barrier measurements, paving the way for efficient analog computing.

On a Generalized Class of Non-Singular Kernel Operators and Their Singular Kernel Extensions: Useful Modeling Insights

Abstract Some possible definitions of fractional derivative operators with non-singular analytic kernels have been introduced. In this paper, we propose a new generalized class of fractional derivative operators of Caputo-type with non-singular analytic kernels which includes some known operators as special cases. We demonstrate a relationship between the fractional derivative operators of the proposed generalized class and the Riemann-Liouville fractional integral operator. We also, using this relationship, introduce the corresponding fractional integral operators. Then, mainly, we provide extensions to the fractional derivative operators of the proposed generalized class that display integrable singular kernels. The extended fractional derivative operators provide useful insights regarding the modeling issue so that the initialization problem can be overcome. Finally, we discuss some basic properties of the proposed operators that are expected to be widely used in fractional calculus.

Rapid Initialization Method of Unmanned Aerial Vehicle Swarm Based on VIO-UWB in Satellite Denial Environment

In environments where satellite signals are blocked, initializing UAV swarms quickly is a technical challenge, especially indoors or in areas with weak satellite signals, making it difficult to establish the relative position of the swarm. Two common methods for initialization are using the camera for joint SLAM initialization, which increases communication burden due to image feature point analysis, and obtaining a rough positional relationship using prior information through a device such as a magnetic compass, which lacks accuracy. In recent years, visual–inertial odometry (VIO) technology has significantly progressed, providing new solutions. With improved computing power and enhanced VIO accuracy, it is now possible to establish the relative position relationship through the movement of drones. This paper proposes a two-stage robust initialization method for swarms of more than four UAVs, suitable for larger-scale satellite denial scenarios. Firstly, the paper analyzes the Cramér–Rao lower bound (CRLB) problem and the moving configuration problem of the cluster to determine the optimal anchor node for the algorithm. Subsequently, a strategy is used to screen anchor nodes that are close to the lower bound of CRLB, and an optimization problem is constructed to solve the position relationship between anchor nodes through the relative motion and ranging relationship between UAVs. This optimization problem includes quadratic constraints as well as linear constraints and is a quadratically constrained quadratic programming problem (QCQP) with high robustness and high precision. After addressing the anchor node problem, this paper simplifies and improves a fast swarm cooperative positioning algorithm, which is faster than the traditional multidimensional scaling (MDS) algorithm. The results of theoretical simulations and actual UAV tests demonstrate that the proposed algorithm is advanced, superior, and effectively solves the UAV swarm initialization problem under the condition of a satellite signal rejection.

Numerical simulation of fractional-order Duffing system with extended Mittag-Leffler derivatives

In this paper, we studied the dynamics of a nonlinear fractional-order Duffing system combined with Mittag-Leffler derivatives in order to provide dynamic behaviors different from existing ones. The Mittag-Leffler derivative is a generalized version of the exponential kernel derivative. To achieve this goal, we introduced a modified extension to higher-order Mittag-Leffler derivatives to overcome the initialization problem. Moreover, we discussed some properties and relationships of the studied derivatives. Then we presented numerical schemes to handle fractional extensions of the considered oscillatory system including the Mittag-Leffler and the Caputo derivatives. Numerical simulations are carried out and the resulting simulation dynamics of the studied fractional oscillatory system are compared.

Multivariate iterative nonlinear chirp mode decomposition: principles and applications

Abstract The rotor-bearing system (RBS) of a hydroelectric unit includes multiple components such as the rotor, rotor shaft, coupling, turbine, and main shaft. There is a strong coupling relationship between the vibration signals of multiple bearing sections. At the same time, the vibration signal of the RBS contains rich frequency components, which can effectively reflect the operating state of the shaft system of the unit. Therefore, synchronous signal analysis across multiple rotor bearing sections is required to completely grasp the operating status of the unit. In this study, the Multivariate Iterative Nonlinear Chirp Mode Decomposition (MINCMD) is proposed to analyze signals on multiple sections simultaneously. MINCMD follows the step-by-step extraction idea of Adaptive Multivariate Chirp Mode Decomposition (AMCMD), but it adopts a new algorithm framework by modifying the objective function. Furthermore, AMCMD does not converge well without a good initial instantaneous frequency (IF). Therefore, a method based on parameterized demodulation (PD) is introduced in MINCMD to solve the multi-channel frequency initialization problem. The effectiveness of the proposed algorithm is verified through a simulation test and real data from hydroelectric units.

Joint track initiation of resolvable group target

This paper studies the problem of resolvable group target track initiation (RGTI) and proposes a novel method to obtain the discrete association vectors and continuous trajectory parameters of individual targets within the group, which benefits from the fitting representations of target tracks and the utilization of group target motion constraint. Specifically, every target track within the group is modeled as a weighted sum of a same set of time basis functions with different weights, and the aim is to solve multiple pairs of association vectors and trajectory parameters, i.e., unknown weights, according to the measurement equation and group target motion constraint. Next, a joint RGTI framework, which includes track extraction, track purification, trajectory parameter and association vector correction, is proposed to transform the original complex constraint set solving problem into multiple solvable sub-problems. And the convergence of RGTI is ensured through iterative optimization of track purification, trajectory parameter correction and association vector correction. Finally, simulation example demonstrates the superiority of the proposed method compared with existing multi-target track initiation methods.

Initiation of decohesion between a flat punch and a thin bonded incompressible layer

Non-axisymmetric frictionless JKR-type adhesive contact between a rigid body and a thin incompressible elastic layer bonded to a rigid base is considered in the framework of the leading-order asymptotic model, which has the form of an overdetermined boundary value problem. Based on the first-order perturbation of the Neumann operator in the Dirichlet problem for Poisson’s equation, the decohesion initiation problem is formulated in the form of a variational inequality. The asymptotic model assumes that the contact zone and its boundary contour during the detachment process are unknown. The absence of the solvability theorem is illustrated by an example of the instability of an axisymmetric flat circular contact.

Numerical simulation of multi‐layer flyer impact initiation of a certain PBX explosive

AbstractThis paper addresses the problem of multi‐layer flyer impact‐induced initiation of a high‐energy explosive PBX‐1(based on 95 % TATB). The CREST reaction combustion model based on the time‐step difference method is embedded in LS‐DYNA, and the pressure history of PBX‐1 in multiple impact initiation is numerically simulated. The strengths and weaknesses of the two models are analyzed by comparing the simulation results of the pressure‐based IG model and the entropy‐based CREST model in multiple impact initiation. The results show that the CREST model is significantly better than the IG model in the problem of multiple impact initiation of PBX‐1, and it matches the experimental results better.

Fast and automatic hesitant fuzzy clustering applied to image segmentation

Image segmentation is a very studied area, looking for the best clustering of pixels. However, it is sometimes a complicated task, especially when these pixels are at the edges of regions, where there is a gradient and it is difficult to decide to which region to assign it. Hesitating fuzzy sets (HFS) better describe these situations, allowing to have multiple possible values for each element, giving more flexibility. This type of sets has been mainly applied in decision-making problems, obtaining better results than other types of fuzzy sets. This research proposes a fast and automatic method based on fuzzy hesitant clustering (FAHFC), which does not require parameters since it is capable of determining the number of clusters, using the Calinski-Harabasz index, in which the segmentation is performed, solving the initialization problem in clustering; it also proposes an alternative to construct the HFS through the use of fuzzy relations. The experiments show superiority in terms of clustering quality and convergence over some selected state-of-the-art algorithms.

A Temperature Prediction Model for Flexible Electronic Devices Based on GA-BP Neural Network and Experimental Verification.

The problem that the thermal safety of flexible electronic devices is difficult to evaluate in real time is addressed in this study by establishing a BP neural network (GA-BPNN) temperature prediction model based on genetic algorithm optimisation. The model uses a BP neural network to fit the functional relationship between the input condition and the steady-state temperature of the equipment and uses a genetic algorithm to optimise the parameter initialisation problem of the BP neural network. To overcome the challenge of the high cost of obtaining experimental data, finite element analysis software is used to simulate the temperature results of the equipment under different working conditions. The prediction variance of the GA-BPNN model does not exceed 0.57 °C and has good robustness, as the model is trained according to the simulation data. The study conducted thermal validation experiments on the temperature prediction model for this flexible electronic device. The device reached steady state after 1200 s of operation at rated power. The error between the predicted and experimental results was less than 0.9 °C, verifying the validity of the model's predictions. Compared with traditional thermal simulation and experimental methods, this model can quickly predict the temperature with a certain accuracy and has outstanding advantages in computational efficiency and integrated application of hardware and software.

Developing a multivariate time series forecasting framework based on stacked autoencoders and multi-phase feature

Time series forecasting across different domains has received massive attention as it eases intelligent decision-making activities. Recurrent neural networks and various deep learning algorithms have been applied to modeling and forecasting multivariate time series data. Due to intricate non-linear patterns and significant variations in the randomness of characteristics across various categories of real-world time series data, achieving effectiveness and robustness simultaneously poses a considerable challenge for specific deep-learning models. We have proposed a novel prediction framework with a multi-phase feature selection technique, a long short-term memory-based autoencoder, and a temporal convolution-based autoencoder to fill this gap. The multi-phase feature selection is applied to retrieve the optimal feature selection and optimal lag window length for different features. Moreover, the customized stacked autoencoder strategy is employed in the model. The first autoencoder is used to resolve the random weight initialization problem. Additionally, the second autoencoder models the temporal relation between non-linear correlated features with convolution networks and recurrent neural networks.Finally, the model's ability to generalize, predict accurately, and perform effectively is validated through experimentation with three distinct real-world time series datasets. In this study, we conducted experiments on three real-world datasets: Energy Appliances, Beijing PM2.5 Concentration, and Solar Radiation. The Energy Appliances dataset consists of 29 attributes with a training size of 15,464 instances and a testing size of 4239 instances. For the Beijing PM2.5 Concentration dataset, there are 18 attributes, with 34,952 instances in the training set and 8760 instances in the testing set. The Solar Radiation dataset comprises 11 attributes, with 22,857 instances in the training set and 9797 instances in the testing set. The experimental setup involved evaluating the performance of forecasting models using two distinct error measures: root mean square error and mean absolute error. To ensure robust evaluation, the errors were calculated at the identical scale of the data. The results of the experiments demonstrate the superiority of the proposed model compared to existing models, as evidenced by significant advantages in various metrics such as mean squared error and mean absolute error. For PM2.5 air quality data, the proposed model's mean absolute error is 7.51 over 12.45, about ∼40% improvement. Similarly, the mean square error for the dataset is improved from 23.75 to 11.62, which is ∼51%of improvement. For the solar radiation dataset, the proposed model resulted in ∼34.7% improvement in means squared error and ∼75% in mean absolute error. The recommended framework demonstrates outstanding capabilities in generalization and outperforms datasets spanning multiple indigenous domains.

Variable-Order Fractional Linear Systems with Distributed Delays—Existence, Uniqueness and Integral Representation of the Solutions

In this work, we study a general class of retarded linear systems with distributed delays and variable-order fractional derivatives of Caputo type. We propose an approach consisting of finding an associated one-parameter family of constant-order fractional systems, which is “almost” equivalent to the considered variable-order system in an appropriate sense. This approach allows us to replace the study of the initial problem (IP) for variable-order fractional systems with the study of an IP for these one-parameter families of constant-order fractional systems. We prove that the initial problem for the variable-order fractional system with a discontinuous initial function possesses a unique continuous solution on the half-axis when the function describing the variable order of differentiation is locally bounded, Lebesgue integrable and has an appropriate decomposition similar to the Lebesgue decomposition of functions with bounded variation. The obtained results lead to the existence and uniqueness of a fundamental matrix for the studied variable-order fractional homogeneous system. As an application of the obtained results, we establish an integral representation of the solutions of the studied IP.

Improving Classification Performance in Dendritic Neuron Models through Practical Initialization Strategies.

A dendritic neuron model (DNM) is a deep neural network model with a unique dendritic tree structure and activation function. Effective initialization of its model parameters is crucial for its learning performance. This work proposes a novel initialization method specifically designed to improve the performance of DNM in classifying high-dimensional data, notable for its simplicity, speed, and straightforward implementation. Extensive experiments on benchmark datasets show that the proposed method outperforms traditional and recent initialization methods, particularly in datasets consisting of high-dimensional data. In addition, valuable insights into the behavior of DNM during training and the impact of initialization on its learning performance are provided. This research contributes to the understanding of the initialization problem in deep learning and provides insights into the development of more effective initialization methods for other types of neural network models. The proposed initialization method can serve as a reference for future research on initialization techniques in deep learning.

Multiple approaches to exploit ferulic acid bio-based epoxy monomer for green thermoset

Bio-based monomers are under investigation to replace fossil-based materials due to the concerns regarding climate change and depletion of fossil raw materials. Lignin, cellulose and hemicellulose represent the main interesting platform to use for developing new monomers due to their significant abundance. Ferulic acid is one of the moieties derived from lignin which can be suitable for many applications. In this study, the ferulic acid was epoxidated and it was investigated in cationic UV-curing. Due to the limited performance obtained during UV-curing, two alternative strategies were developed to overcome the initial problem of poor material properties. A thiol-ene epoxy system based on the ferulic epoxy derivative and a commercially available thiol as well as a thermally cured system based on pure cationic curing of ferulic acid diepoxy were chosen as alternative methods. The different curing processes were thoroughly investigated by means of FTIR (Fourier transform infrared spectroscopy) and photo-DSC (differential scanning calorimetry). The thermo-mechanical properties of the thermosets employing DMA- (dynamic mechanical analysis) and tensile analysis were deeply evaluated. Finally, the possibility to use the best cured system as an adhesive was raised investigating the shear strength of metallic and composite joined samples using the single lap offset (SLO) test under compression.

Research on optimization method for flyby observation mission adapted to satellite on-board computation

In this paper, the optimization problem of orbital transfer strategy for orbital flyby observation missions is studied. A hybrid optimization method is proposed, which is improved to make it more suitable for satellite on-board computing. This new algorithm is designed to solve the initial value sensitivity problem of the sequential quadratic programming algorithm (SQP). It is consisted of the depth-first search algorithm (DFS) and the SQP algorithm and thus has the characteristics of fast convergence, high reliability, and good robustness. With this method, the DFS with a large step size is calculated first, and then the optimal value in the calculation result is used as the initial value of the SQP algorithm for further optimization. This method can obtain the approximate optimal solution available in engineering. The numerical simulation of an orbital transfer optimization problem is set to verify the effectiveness of the new hybrid algorithm. The simulation results compared with the genetic algorithm (GA) show that the proposed hybrid algorithm can effectively reduce the on-board resource occupation when getting similar results and thus can meet the needs of satellite on-board computing.

Enhancing security for physical layer communication in RIS-aided MIMO-NOMA systems in the presence of an eavesdropper

This paper investigates the potential of leveraging a reconfigurable intelligent surface (RIS) in the multiple-input multiple-output non-orthogonal multiple access (MIMO-NOMA) wireless communication systems. In fact, NOMA improves spectral efficiency (SE) through power domain utilization, and RIS enhances the system performance by manipulating the propagation environment. In this paper, we investigate the physical layer security (PLS) of a RIS-aided MIMO-NOMA system serving two users in the presence of an eavesdropper (EV), where the channel state information (CSI) of the EV is unknown for the BS. To address the security challenge, we utilize secrecy outage probability (SOP) and minimize it by optimizing the RIS’s phase shift matrix, the beamforming vector, and the power allocation coefficients. Due to the non-convexity of the main optimization problem, we decompose the initial problem into three subproblems, and then, we propose an iterative approach to determine the unknown parameters alternately. The first subproblem involves obtaining the optimal beamforming vector and phase shift matrix using the semidefinite relaxation (SDR) method, which introduces high complexity. As an alternative, another algorithm is proposed based on the duality of the optimization problem, offering lower complexity and better accuracy for high-dimensional optimization problems. In this algorithm, the successive convex approximation (SCA) technique is employed to achieve the optimum phase shift matrix, and the stochastic gradient (SG) approach is used to determine the beamforming vector. Furthermore, the paper establishes the optimal values of power allocation coefficients by minimizing the difference between the SOPs of the two users. Finally, the simulation results demonstrate that the proposed power allocation coefficients scheme significantly enhances system performance compared to the existing fixed power allocation coefficients scheme.

Evolution of initial discontinuities in a particular case of two-step initial problem for the defocusing complex modified KdV equation

In this paper, the complete classification of solutions of defocusing complex modified KdV equation with a particular case of two-step initial condition is investigated by the finite-gap integration approach and Whitham modulation theory. The periodic wave solution and corresponding Whitham modulation equations for zero-phase, one-phase, two-phase are found. The self-similar wave structures in each case are composed of different building blocks: the plateau, vacuum, rarefaction wave, dispersive shock wave and two-genus region, which are studied in detail. The results of direct numerical simulations for the defocusing complex modified KdV equation agree well with those of Whitham modulation theory.