Range Dependence Research Articles

The Method for Obtaining Shot Cloud Time Span and Range Dependence

When studying the behavior of a shot cloud in space, a variety of information and measurement systems can be applied, for example, based on light screens (photoelectronic blocking devices of a small ballistic measuring installation consisting of a linear emitter and an optical sensor based on a photodiode). To study the process of shot cloud movement in space and to assess its parameters (for example, length at a given range from the muzzle) correctly, it is advisable to have a simulation model of the optical sensor signal when the light screen is crossed by a shot cloud. Taking into account previous studies, it is necessary to know the magnitude of the scale parameter in the Rayleigh distribution describing the shot cloud in the direction of firing in time in order to obtain a similar model at some distance from the muzzle. A method that allows obtaining function of dependence of the mentioned scale parameter and the distance from the muzzle and based on real data from several optical sensors of light screens installed on the shooting track is proposed. The example of application the proposed method to obtain similar functions based on the results of processing optical sensor signals obtained in real experiments for cartridges loaded with three different shot types is given. The obtained dependences were approximated by polynomials of the first and second degree using the least squares method, and the error was estimated for various types of cartridges. Based on the obtained results, specific recommendations for the practical application of the developed method are proposed. The estimation of the confidence interval width for the expectation function of the shot cloud time span is performed. The ways of creating advanced signal simulation models from optical sensors of light screens when shooting with shot are outlined.

Transparent Tb2Ti2O7 ceramics for use in Faraday isolators

Magneto-optical and thermo-optical characteristics of transparent Tb2Ti2O7 ceramics were investigated. The dependence of the index of refraction on the wavelength in the 0.29–2 μm range, the wavelength and temperature dependence of the Verdet constant, as well as the dependence of thermally induced depolarization on laser radiation power were measured. The value of the Verdet constant in Tb2Ti2O7 surpasses that in Tb3Ga5O12 by more than 1.68 times. The thermo-optical characteristic Qeff was estimated to be (1.8–3.7)∙10−8 1/K, which is record small compared to Qeff of the known magneto-optical materials. The small value of Qeff makes Tb2Ti2O7 a highly promising magneto-optical material for Faraday isolators and rotators for high average power lasers.

Mitigating the impact of image processing variations on tumour [18F]-FDG-PET radiomic feature robustness

Radiomics analysis of [18F]-fluorodeoxyglucose ([18F]-FDG) PET images could be leveraged for personalised cancer medicine. However, the inherent sensitivity of radiomic features to intensity discretisation and voxel interpolation complicates its clinical translation. In this work, we evaluated the robustness of tumour [18F]-FDG-PET radiomic features to 174 different variations in intensity resolution or voxel size, and determined whether implementing parameter range conditions or dependency corrections could improve their robustness. Using 485 patient images spanning three cancer types: non-small cell lung cancer (NSCLC), melanoma, and lymphoma, we observed features were more sensitive to intensity discretisation than voxel interpolation, especially texture features. In most of our investigations, the majority of non-robust features could be made robust by applying parameter range conditions. Correctable features, which were generally fewer than conditionally robust, showed systematic dependence on bin configuration or voxel size that could be minimised by applying corrections based on simple mathematical equations. Melanoma images exhibited limited robustness and correctability relative to NSCLC and lymphoma. Our study provides an in-depth characterisation of the sensitivity of [18F]-FDG-PET features to image processing variations and reinforces the need for careful selection of imaging biomarkers prior to any clinical application.

Bi-SeqCNN: A Novel Light-weight Bi-directional CNN Architecture for Protein Function Prediction.

Deep learning approaches, such as convolution neural networks (CNNs) and deep recurrent neural networks (RNNs), have been the backbone for predicting protein function, with promising state-of-the-art (SOTA) results. RNNs with an in-built ability (i) focus on past information, (ii) collect both short-and-long range dependency information, and (iii) bi-directional processing offers a strong sequential processing mechanism. CNNs, however, are confined to focusing on short-term information from both the past and the future, although they offer parallelism. Therefore, a novel bi-directional CNN that strictly complies with the sequential processing mechanism of RNNs is introduced and is used for developing a protein function prediction framework, Bi-SeqCNN. This is a sub-sequence-based framework. Further, Bi-SeqCNN + is an ensemble approach to better the prediction results. To our knowledge, this is the first time bi-directional CNNs are employed for general temporal data analysis and not just for protein sequences. The proposed architecture produces improvements up to +5.5% over contemporary SOTA methods on three benchmark protein sequence datasets. Moreover, it is substantially lighter and attain these results with (0.50-0.70 times) fewer parameters than the SOTA methods.

Long-range Dependence in the Carbon Emission Market

This paper investigates the long-range dependence of returns and the volatility of the carbon emission permits in the Chicago Climate Exchange. It is found that contracts with different vintages demonstrate significantly variant long memory properties. In particular, strong long memory is found for vintages from 2003 to 2006, while no evidence of long range dependence in the return series is found for vintage 2007 to vintage 2010.

Convergence analysis of a fully discrete scheme for diffusion-wave equation forced by tempered fractional Brownian motion

In this paper, we investigate a fully discrete approximation of stochastic diffusion-wave equation driven by additive tempered fractional Gaussian noise. This additive noise exhibits semi-long range dependence. The model involves two nonlocal terms in time, i.e., a Caputo fractional derivative and a tempered fractional Brownian motion. The space discretization is achieved via the spectral Galerkin method. The Caputo fractional derivative of order α∈(1,2) is approximated by using the Grünwald–Letnikov formula. Combining Mittag-Leffler function, Laplace transform and z-transform, we establish the error estimates of the full discretization in the sense of mean-squared L2-norm. Numerical experiments are presented to confirm the strong convergence rates.

A note on oriented percolation with inhomogeneities and strict inequalities

This note was motivated by natural questions related to oriented percolation on a layered environment that introduces long range dependence. As a convenient tool, we are led to deal with questions on the strict decrease of the percolation parameter in the oriented setup when an extra dimension is added.

A new shape of extremal clusters for certain stationary semi-exponential processes with moderate long range dependence

A new shape of extremal clusters for certain stationary semi-exponential processes with moderate long range dependence

Robust inference of panel data models with interactive fixed effects under long memory: A frequency domain approach

This paper studies a linear panel data model with interactive fixed effects wherein regressors, factors and idiosyncratic error terms are all stationary but with potential long memory. The setup involves a new formulation of panel data models, where weakly dependent regressors, factors and idiosyncratic errors are embedded as a special case. Standard methods based on principal component decomposition and least squares estimation, as in Bai (2009), are found to be biased and distorted in inference. To cope with this failure and to provide a simple implementable estimation procedure, a frequency domain least squares estimation is proposed. The limit distribution of the frequency domain estimator is established and a self-normalized approach to inference without the need for plug-in estimation of memory parameters is developed. Simulations show that the frequency domain estimator performs robustly under short memory and outperforms the time domain estimator when long range dependence is present. An empirical illustration is provided, examining the long-run relationship between stock returns and realized volatility.

Reweighted Extreme Learning Machine-Based Clutter Suppression and Range Compensation Algorithm for Non-Side-Looking Airborne Radar

Non-side-looking airborne radar provides important applications on account of its all-round multi-angle airspace coverage. However, it suffers clutter range dependence that makes the samples fail to satisfy the condition of being independent and identically distributed (IID), and it severely degrades traditional approaches to clutter suppression and target detection. In this paper, a novel reweighted extreme learning machine (ELM)-based clutter suppression and range compensation algorithm is proposed for non-side-looking airborne radar. The proposed method involves first designing the pre-processing stage, the special reweighted complex-valued activation function containing an unknown range compensation matrix, and two new objective outputs for constructing an initial reweighted ELM-based network with its training. Then, two other objective outputs, a new loss function, and a reverse feedback framework driven by the specifically designed objectives are proposed for the unknown range compensation matrix. Finally, aiming to estimate and reconstruct the unknown compensation matrix, special processes of the complex-valued structures and the theoretical derivations are designed and analyzed in detail. Consequently, with the updated and compensated samples, further processing including space–time adaptive processing (STAP) can be performed for clutter suppression and target detection. Compared with the classic relevant methods, the proposed algorithm achieves significantly superior performance with reasonable computation time. It provides an obviously higher detection probability and better improvement factor (IF). The simulation results verify that the proposed algorithm is effective and has many advantages.

The Acoustic Laboratory for Marine Applications (ALMA) applied to fluctuating environment analysis

The Acoustic Laboratory for Marine Applications (ALMA) has been used to address problems in underwater acoustics, such as sound propagation in fluctuating environments. In this work, data from the ALMA-2016 at-sea campaign are used to analyze the ocean fluctuation's influence on sound propagation in a shallow-water waveguide. The experiment took place in November 2016 on the continental shelf of the eastern coast of the island of Corsica. A source and a receiver array were 9.3 km apart in a nearly constant water depth of 100 m. A thermistor chain was moored near the source to monitor sound speed fluctuations. The source emitted a variety of signals from which the chirp (1–13 kHz) is used to extract the waveguide eigenrays. To do so, a time-domain beamforming is performed on the match-filtered received signals with an automatic detection of local maxima in the time of arrival/direction of arrival (TOA/DOA) domain. A 2 min acquisition period of more than 13 h duration shows significant fluctuations in eigenray TOAs/DOAs. Qualitative comparisons with synthetic signals obtained from simulations permit reproduction of the observed eigenray fluctuations without including range dependence of the sound-speed profile. In addition, the joint analysis of the probability density function of the normalized acoustic intensity and of the thermistor chain data highlights the time dependence of the received signal characteristics.

LRD spectral analysis of multifractional functional time series on manifolds

This paper addresses the estimation of the second-order structure of a manifold cross-time random field (RF) displaying spatially varying Long Range Dependence (LRD), adopting the functional time series framework introduced in Ruiz-Medina (Fract Calc Appl Anal 25:1426–1458, 2022). Conditions for the asymptotic unbiasedness of the integrated periodogram operator in the Hilbert–Schmidt operator norm are derived beyond structural assumptions. Weak-consistent estimation of the long-memory operator is achieved under a semiparametric functional spectral framework in the Gaussian context. The case where the projected manifold process can display Short Range Dependence (SRD) and LRD at different manifold scales is also analyzed. The performance of both estimation procedures is illustrated in the simulation study, in the context of multifractionally integrated spherical functional autoregressive–moving average (SPHARMA(p,q)) processes.

A continuum damage coupled unified viscoplastic model for simulating the mechanical behaviour of a ductile cast iron under isothermal low-cycle fatigue, fatigue-creep and creep loading

A novel continuum damage coupled unified viscoplastic model is used for simulating the stress–strain responses of SiMo 4.06 cast iron under Low-Cycle Fatigue (LCF), fatigue-creep and creep loading for temperatures up to 650 °C. The advanced constitutive model that is developed within the framework of the Chaboche model allows the simulation of various effects including strain rate sensitivity, cyclic hardening and softening, static recovery and strain range dependency. The hyperbolic sine flow rule and the static recovery of kinematic hardening rule are proposed in order to effectively simulate both stress relaxation and creep strains. The isotropic damage variable is introduced into the constitutive equations to represent the effects of material degradation. Three main damage mechanisms are considered: fatigue, creep and ductile damage. In addition, the model is further modified to take into account the progressive microdefects closure effect. Finally, the prediction capability of the proposed model is illustrated for the experimental data obtained from the various uniaxial material tests. A good correlation was achieved between the simulated and the experimental results.

Using LSTM neural network to generate music

Music generation is a cutting edge and useful research filed, which is helpful for artists to compose novel melodies as well as revealing potential patterns of music. Recurrent neural network (RNN) is a member of the neural network family, which is commonly used for processing sequential data. It can deal with sequential changes in data compared to normal neural networks. Long short-term memory (LSTM) aims at improving the conventional RNN. It is designed to alleviate the deficiencies of gradient disappearance and gradient explosion that possibly happened in RNN during training. In simple terms, LSTM is superior at grasping long term information than normal RNN. It can record the information that requires to be recorded for a long time and abandon these unimportant features. Unlike RNN, which have merely one way of stacking long-term information. It's quite useful for tasks that require long range dependence. In this work the effectiveness of the LSTM is validated on the music generation task.

Intermolecular interactions and the weakly bound precursor states of elementary physicochemical processes

This study concerns the importance of the precursor (or pre-reactive) state of elementary physicochemical processes whose basic features, as structure, stability, and trapping effect of reagents, are controlled by the balance of intermolecular forces that arise at long range and operate at intermediate and short separation distances. The detailed formulation of such forces, determining formation probability and dynamical evolution of the precursor state, is of relevance in molecular science and difficult to be treated by quantum chemistry. Such a problem has been tackled by us exploiting the phenomenological approach, which employs semi-empirical and empirical formulas to represent strength, range and angular dependence of the leading interaction components involved. In addition to the study of transport phenomena, part of the attention is addressed to chemi-ionization (or Penning ionization) reactions for which neutral reagents lead to atomic and/or molecular ions plus electrons as products. Chemi-ionizations are bimolecular processes occurring in several environments of interest, where a reagent is a species, formed in excited-metastable electronic states by collisions with energetic electrons or cosmic rays. For such reactions all crucial electronic rearrangements, affecting stability and evolution of the weakly bound precursor state, here coincident with the reaction transition state, are characterized with a high detail. The results of the present study are of interest for many other processes, whose precursor states and their relevant features are difficult to characterize, often masked by several other effects.Graphical abstract

Detection of long range dependence in the time domain for (in)finite-variance time series

Empirical detection of long range dependence (LRD) of a time series often consists of deciding whether an estimate of the memory parameter d corresponds to LRD. Surprisingly, the literature offers only a few estimators for d in the time domain. And those that exist are criticized for relying on visual inspection to determine an observation window [ n 1 , n 2 ] for a linear regression. Here, we provide rigorous asymptotic conditions on [ n 1 , n 2 ] to ensure consistency of the well-known variance plot estimator under LRD. We do this for a large class of square-integrable time series models so that the estimator can be used to detect LRD for infinite-variance time series in the sense of indicators of excursion sets. Thus, detection of LRD for infinite-variance time series is another novelty of our paper. Finally, a simulation study compares the variance plot estimator with a popular spectral domain estimator.

Multi-scale moving target detection with FDA-MIMO radar

The recently developed frequency diverse array (FDA) radar applies a small frequency increment across the array elements, which provides advantages in clutter suppression, jamming suppression, and target detection for existing radar systems. In this paper, the frequency increment is enlarged to guarantee that the transmit waveforms are orthogonal in frequency domain for multiple-input multiple-output (MIMO) radar. A multi-scale moving target detection method of FDA-MIMO radar is proposed. The FDA radar with a large frequency increment suffers from the deteriorated decorrelation of scatterers within the same range bin and causes the inner-bin range dependence (IRD) property in transmit spatial domain. By taking additional space–time snapshots in transmit domain as training samples, the space–time adaptive processing (STAP) is applied in joint receive and Doppler domain for clutter suppression, which alleviates the requirement of insufficient training samples in non-stationary clutter environment. In the sequel, a set of beamformers within the whole angular domain of interest is utilized for target detection, which achieves target detection under the condition of random target locations and sizes. Numerical simulation results are provided to verify the effectiveness of the proposed method.

Numerical predictions of low cyclic fatigue and creep-fatigue behavior of P92 steel under non-isothermal-mechanical loading

Low-Cycle Fatigue (LCF) and creep-fatigue-interaction (CFI) behavior of a ferritic-martensitic stainless steel P92 is studied under strain-controlled non-isothermal -mechanical fatigue loading in the temperature range of 300°C∼620°C and 500°C∼620°C, respectively. The strain-controlled tests with and without the tensile and compression holding times are simulated for different mechanical strains amplitudes. An improved Unified Viscoplastic constitutive Model (UVM) is proposed based on the framework of modified Chaboche non-linear kinematic hardening rule. The improved UVM incorporates strain rate sensitivity, temperature rate dependence, static recovery associated with plastic strain, mean stress evolution and cyclic softening of strain range dependence, and so on. Furthermore, the expressions of the implicit stress integration algorithm and the consistent tangential modulus are deduced on the basis of radial return mapping and backward Euler integration methods, respectively, and the improved UVM is implemented into the ABAQUS software using the user material subroutine UMAT. The simulation results of the UVM correlate well with the experimental data.

Sparse mixed attention aggregation network for multimodal images fusion tracking

Recent years have witnessed the exciting performance of trackers based on Transformer. However, they usually separate the process of information extraction and integration, weakening the information interaction between the target and search region. In addition, they depend on traditional Transformer to model the long range dependency, which leads to a lack of focus on the primary information needed by high-accuracy trackers. In this paper, a sparse mixed attention aggregation model is proposed for robust tracking based on visible and thermal infrared images. To be specific, a backbone network composed of sparse mixed attention is designed to achieve information extraction and integration. This is helpful to obtain specific discriminative feature information and enhance their communication. To give full play to the complementary visible and thermal information, a confidence aware aggregation network is designed, which can learn the reliable confidence of visible and thermal branches. Finally, a corner-based localization head is introduced to estimate the target state. Extensive experiments on three large-scale multimodal tracking benchmarks demonstrate the superior tracking ability of the proposed tracker over other advanced trackers.

A novel vertical element-pulse coding scheme for range-ambiguous clutter elimination

The coexistence of clutter range ambiguity and range dependence in the airborne forward-looking radar may render the common space–time adaptive processing (STAP) ineffective. To cope with this challenge, a novel vertical element-pulse coding (EPC) radar is proposed in our paper. Note that both the elevation angles and coding factors determine the transmitted spatial frequency of the proposed vertical EPC radar. Therefore, the clutters corresponding to different ambiguous areas can be distinguished in the spatial frequency dimension based on this property. The element-pulse decoding and pre-STAP filtering are devised to separate and extract these clutters corresponding to different ambiguous areas. In the following, the clutter elimination is independently accomplished for each ambiguous area that has been separated. Consequently, the clutter range ambiguity as well as clutter range dependence are addressed simultaneously. The efficacy of the proposed approach is verified with simulation experiments.