Jump Process Research Articles

AMPLITUDE ADJUSTMENT WITH FIWASVJ MODEL

Andrianantenainarinoro\cite{maReference101} remarked that the price amplitudes of financial models may not correspond to the reality and we propose here a model in continuous time Fractionally Integrated WASC Stochastic Volatility Jump. To do this, we introduce a fractal index in the WASC Stochastic Volatility Jump model and we have two others characteristics: amplitude adjustment and memory of process. We present also several theories in stochastic calculus, algebraic, differential geometry, numerical method and estimating method which can use to financial such us: sense of a fractional integral, relationship between trace and determinant operator, Euler's approximation for an unresolved differential equation and convergence speed.

Co-Evolution of Parallel Triple Subduction Systems in the New Guinea Region: A Systematic Numerical Study

A specific configuration of the global subduction system is the parallel triple subduction. The widely accepted example of parallel triple subduction is the New Guinea region, including a northward dip at the New Britain Trench (NBT), a southward dip at the Trobriand Trough (TT), and North Solomon Trench (NST). Questions regarding the parallel triple subduction system remain largely unexplored in terms of factors controlling its initiation, duration, and dynamics. Here, we used two-dimensional numerical models to study the dynamics mechanism of the parallel triple subduction system in the New Guinea region. Four possible regimes were achieved: 1) the double subduction model, which includes the forward subduction jumping model (FSJ) and the subduction polarity reversal model (SPR) and 2) the parallel triple subduction model, which includes the tendency to the forward jumping model (TFSJ) and the tendency to polarity reversal (TSPR). By evaluating the four regimes with actual seismic data, we suggest that the pre-existing rupture and length of ocean–continent transition (OCT) determine the formation of the TT, while the formation of the NBT may be the result of the rheological strength differences between the Solomon Island Arc (SIA) and Solomon Sea Basin (SSB); the initial length of the SSB can regulate the competitive relationship between the TT and NBT, which also determines the present-day inactive state of the TT. A longer SSB makes the TT and NBT initiated independently, while a narrower SSB will allow interaction during subduction initiation of the TT and NBT.

Ultrametric diffusion, rugged energy landscapes and transition networks

In this article we introduce the ultrametric networks which are p-adic continuous analogs of the standard Markov state models constructed using master equations. A p-adic transition network (or an ultrametric network) is a model of a complex system consisting of a hierarchical energy landscape, a Markov process on the energy landscape, and a master equation. The energy landscape consists of a finite number of basins. Each basin is formed by infinitely many network configurations organized hierarchically in an infinite regular tree. The transitions between the basins are determined by a transition density matrix, whose entries are functions defined on the energy landscape. The Markov process in the energy landscape encodes the temporal evolution of the network as random transitions between configurations from the energy landscape. The master equation describes the time evolution of the density of the configurations. We focus on networks where the transition rates between two different basins are constant functions, and the jumping process inside of each basin is controlled by a p-adic radial function. We solve explicitly the Cauchy problem for the master equation attached to this type of networks. The solution of this problem is the network response to a given initial concentration. If the Markov process attached to the network is conservative, the long term response of the network is controlled by a Markov chain. If the process is not conservative the network has absorbing states. We define an absorbing time, which depends on the initial concentration, if this time is finite the network reaches an absorbing state in a finite time. We identify in the response of the network the terms responsible for bringing the network to an absorbing state, we call them the fast transition modes. The existence of the fast transition modes is a consequence of the assumption that the energy landscape is ultrametric (hierarchical), and to the best of our understanding this result cannot be obtained by using standard methods of Markov state models. Nowadays, it is widely accepted that protein native states are kinetic hubs that can be reached quickly from any other state. The existence of fast transition modes implies that certain states on an ultrametric network work as kinetic hubs.

Deterministic Network Calculus-Based H ∞ Load Frequency Control of Multiarea Power Systems Under Malicious DoS Attacks

In multiarea power systems, by preempting the transmission resources, denial-of-service (DoS) attacks may cause continuous packet losses in communication network and correspondingly make a performance degradation or even instability of the load frequency control (LFC) system. In this paper, a LFC scheme resilient to DoS attacks is developed. First, the dynamic boundary behaviors of data flow transmitted among multiple switches in normal no-DoS attack scenarios are described by using deterministic network calculus. Then, the analytic relationships among the upper transmission delay bound and key network parameters (e.g., packet size, forwarding rate, and hops) are deduced. Second, considering that the influences of bounded transmission delays on control instructions, the dynamics of closed-loop LFC systems are described as a series of subsystems with actual delays as the jumping decision variables. The established jumping model clearly demonstrates the dynamic evolution process of LFC systems under DoS attacks and stochastic delays. The <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$H_{\infty }$ </tex-math></inline-formula> -stabilization criteria considering parameter uncertainties are further deduced by using the Lyapunov theory and a constrained optimization algorithm is developed. Finally, case studies show the effectiveness of the proposed method.

Functional Morphology of the Thorax of the Click Beetle Campsosternus auratus (Coleoptera, Elateridae), with an Emphasis on Its Jumping Mechanism.

Simple SummaryClick beetles are well-known for the specialized thoracic structure, which they can click to thrust themselves into the air and to right themselves. Several aspects of their jumping mechanism were still not entirely clear prior to this study. We utilized traditional dissection, 3D virtual dissection, and high-speed filming techniques to investigate the functional morphology of their thorax. Our results show several new insights into their extraordinary clicking and jumping mechanisms. We investigated and described the thoracic structures, jumping mechanism, and promesothoracic interlocking mechanism of the click beetle Campsosternus auratus (Drury) (Elateridae: Dendrometrinae). Two experiments were conducted to reveal the critical muscles and sclerites involved in the jumping mechanism. They showed that M2 and M4 are essential clicking-related muscles. The prosternal process, the prosternal rest of the mesoventrite, the mesoventral cavity, the base of the elytra, and the posterodorsal evagination of the pronotum are critical clicking-related sclerites. The destruction of any of these muscles and sclerites resulted in the loss of normal clicking and jumping ability. The mesonotum was identified as a highly specialized saddle-shaped biological spring that can store elastic energy and release it abruptly. During the jumping process of C. auratus, M2 contracts to establish and latch the clicking system, and M4 contracts to generate energy. The specialized thoracic biological springs (e.g., the prosternum and mesonotum) and elastic cuticles store and abruptly release the colossal energy, which explosively raises the beetle body in a few milliseconds. The specialized trigger muscle for the release of the clicking was not found; our study supports the theory that the triggering of the clicking is due to the building-up of tension (i.e., elastic energy) in the system.

Valuation of Annuity Guarantees Under a Self-Exciting Switching Jump Model

This article investigates the valuation of annuity guarantees under a regime-switching model when the dynamics of the underlying stock price follow a self-exciting switching jump-diffusion process. In this framework, we add a jump component to a regime-switching geometric Brownian for large shocks on the stock price. The intensity of shock arrivals is a Hawkes process modulated by a continuous time hidden Markov chain with a finite number of states. The interest rate used for discounting is stochastic and correlated to the stock market. In an incomplete market, we define an equivalent martingale measure to price a variable annuity contract that guarantees a minimum living or death benefit. Under this equivalent martingale measure, we propose closed-form approximation formulas using the inverse Fourier transform technique. A numerical implementation highlights the impact of self-exciting jumps and economic regimes on the valuation of guarantees.

FILTERING OF SIGNALS WITH UNKNOWN INTERFERENCE POWER AND RANDOM JUMPING PERTURBATION

A linear stochastic dynamic system under the influence of random disturbances and interference is considered. The perturbation is a sequence of uncorrelated random variables with a distribution in the range [–1, 1]. This sequence is modeled by a random binary signal with values 1 and 0 and is described by a Markov chain with known probabilities of transitions from one state to another. The modulated signal is fed to the input of the linear control unit. The output signal of the control object is measured with an error, which is a sequence of uncorrelated random variables with an unknown distribution in the range [–1, 1].The problem under consideration differs from the optimal linear filtration problems based on the application of the Kalman filter and its modifications. Its novelty consists in the following: 1) the input signal is a random jumping process – uncorrelated noise modulated by a random binary signal; 2) the variances of random processes – the input signal and the interference characterizing their power are unknown. А posteriori mathematical expectation and the a posteriori variance of the filtering error are determined by the methods of Bayesian estimation and the theory of systems with a random jump structure. The optimal estimation algorithm is described by a system of recurrent equations. It consists of five interconnected blocks: 1) a meter of mathematical expectation and dispersion of an additive mixture of an output signal with interference; 2) an indicator of a random structure; 3) a classifier of a random structure; 4) a dispersiometer; 5) a filter.

Stability Results for Symmetric Jump Processes on Metric Measure Spaces with Atoms

Stability Results for Symmetric Jump Processes on Metric Measure Spaces with Atoms

Jump processes as generalized gradient flows

We have created a functional framework for a class of non-metric gradient systems. The state space is a space of nonnegative measures, and the class of systems includes the Forward Kolmogorov equations for the laws of Markov jump processes on Polish spaces. This framework comprises a definition of a notion of solutions, a method to prove existence, and an archetype uniqueness result. We do this by using only the structure that is provided directly by the dissipation functional, which need not be homogeneous, and we do not appeal to any metric structure.

Time Discretized Variational Iteration Method for the Stochastic Volatility Process with Jumps

A model for both stochastic jumps and volatility for equity returns in the area of option pricing is the stochastic volatility process with jumps (SVPJ). A major advantage of this model lies in the area of mean reversion and volatility clustering between returns and volatility with uphill movements in price asserts. Thus, in this article, we propose to solve the SVPJ model numerically through a discretized variational iteration method (DVIM) to obtain sample paths for the state variable and variance process at various timesteps and replications in order to estimate the expected jump times at various iterates resulting from executing the DVIM as n increases. These jumps help in estimating the degree of randomness in the financial market. It was observed that the average computed expected jump times for the state variable and variance process is moderated by the parameters (variance process through mean reversion), Θ (long-run mean of the variance process), σ (volatility variance process) and λ (constant intensity of the Poisson process) at each iterate. For instance, when = 0.0, Θ = 0.0, σ = 0.0 and λ = 1.0, the state variable cluttered maximally compared to the variance process with less volatility cluttering with an average computed expected jump times of 52.40607869 as n increases in the DVIM scheme. Similarly, when = 3.99, Θ = 0.014, σ = 0.27 and λ = 0.11, the stochastic jumps for the state variable are less cluttered compared to the variance process with maximum volatility cluttering as n increases in the DVIM scheme. In terms of option pricing, the value 52.40607869 suggest a better bargain compared to the value 20.40344029 due to the fact that it yields less volatility rate. MAPLE 18 software was used for all computations in this research.

Phishing short URL detection based on link jumping on social networks

Nowadays, a large number of people frequently use social networks. Social networks have become important platforms for people to publish and obtain information. However, social networks have also become the main venue for hackers to initiate online fraud. Phishing is a common way used by hackers to launch online fraud on social networks. This paper proposes a method for detecting phishing short URL based on the link jumping. The method uses a hierarchical hidden Markov model with two-layer structure to describe the link jumping process after user clicking on short URL, so as to identify phishing short URL on social networks. The proposed method includes a training phase and an identification phase. In the identification phase, the average log-likelihood probability of the observation sequence is calculated. An experiment based on real datasets of Weibo is conducted to evaluate this method. The experiment results validate the effectiveness of this method.

Computational Complexity of Jumping Block Puzzles

In combinatorial reconfiguration, the reconfiguration problems on a vertex subset (e.g., an independent set) are well investigated. In these problems, some tokens are placed on a subset of vertices of the graph, and there are three natural reconfiguration rules called ``token sliding,'' ``token jumping,'' and ``token addition and removal''. In the context of computational complexity of puzzles, the sliding block puzzles play an important role. Depending on the rules and set of pieces, the sliding block puzzles characterize the computational complexity classes including P, NP, and PSPACE. The sliding block puzzles correspond to the token sliding model in the context of combinatorial reconfiguration. On the other hand, a relatively new notion of jumping block puzzles is proposed in puzzle society. This is the counterpart to the token jumping model of the combinatorial reconfiguration problems in the context of block puzzles. We investigate several variants of jumping block puzzles and determine their computational complexities.

Pure-jump semimartingales

A new integral with respect to an integer-valued random measure is introduced. In contrast to the finite variation integral ubiquitous in semimartingale theory (Jacod and Shiryaev, 2003, II.1.5), the new integral is closed under stochastic integration, composition, and smooth transformations. The new integral gives rise to a previously unstudied class of pure-jump processes: the sigma-locally finite variation pure-jump processes. As an application, it is shown that every semimartingale $X$ has a unique decomposition $$X = X_0 + X^{\mathrm{qc}}+X^{\mathrm{dp}},$$ where $X^{\mathrm{qc}}$ is quasi-left-continuous and $X^{\mathrm{dp}}$ is a sigma-locally finite variation pure-jump process that jumps only at predictable times, both starting at zero. The decomposition mirrors the classical result for local martingales (Yoeurp, 1976, Theoreme~1.4) and gives a rigorous meaning to the notions of continuous-time and discrete-time components of a semimartingale. Against this backdrop, the paper investigates a wider class of processes that are equal to the sum of their jumps in the semimartingale topology and constructs a taxonomic hierarchy of pure-jump semimartingales.

Effects of the surface tension gradient and viscosity on coalescence-induced droplet jumping on superamphiphobic surfaces

With the development of superhydrophobic surface preparation technology, coalescence-induced droplet jumping shows broad application prospects in the fields of enhanced condensation heat transfer and self-cleaning. In this work, the coalescence-induced jumping process of heterogeneous and homogeneous droplets on superamphiphobic surfaces was studied by using glycerol–water mixtures with different glycerol volume fractions. The results showed that the surface tension gradient of heterogeneous droplets will lead to asymmetric deformation of droplets, asymmetric distribution of internal pressure of droplets, as well as decrease in the energy conversion efficiency and the vertical departure velocity. Our study also revealed that the effects of surface tension gradient and viscosity on droplet jumping are different in the two regions. When the glycerol volume fraction is less than 40%, the droplet velocity and energy conversion are dominated by the surface tension gradient, and the vertical departure velocity and the energy conversion efficiency of homogeneous droplets are larger. When the glycerol volume fraction is greater than 40%, the droplet velocity and energy conversion are dominated by the surface tension gradient and viscosity together, and the vertical departure velocity and the energy conversion efficiency of heterogeneous droplets are larger.

Forecasting of solar radiation in photovoltaic power station based on ground‐based cloud images and BP neural network

The solar radiation near the surface is the main reason that affects photovoltaic power generation. Accurate ultra-short-term solar radiation prediction is the premise of photovoltaic power generation prediction. Here the cloud movement prediction method based on the ground-based cloud images is presented. The cloud recognition, cloud matching, cloud area correction and cloud movement prediction are performed to predict the drift trajectory of the clouds that will block the sun. Then, using digital image technology, 13 kinds of feature information are extracted from the ground-based cloud images. Then, these feature information are input into BP neural network, and the parameters of BP neural network are optimized by genetic algorithm. Through a large number of data training, a new ultra-short-term prediction model of solar radiation is established. Finally, through experimental comparison, the results show that the prediction accuracy of the model with the feature information of ground-based cloud images can reach 96%, compared with the model without the feature information of ground-based cloud images, the accuracy is improved by 5%. The proposed ultra-short-term solar radiation prediction model can effectively predict the radiation jumping process caused by cloud occlusion, and greatly improve the prediction accuracy, especially in cloudy weather.

Microscopic dynamics of lithium diffusion in single crystal of the solid-state electrolyte La2/3−xLi3xTiO3 ( x=0.13 ) studied by quasielastic neutron scattering

Quasielastic neutron scattering (QENS) measurements combined with first principles based moleculardynamics calculations were conducted to study the dynamics of Li$^+$ ions in a solid-state electrolyte La$_{2/3-x}$Li$_{3x}$TiO$_{3}$ (LLTO) with $x=0.13$. By using a large $^7$Li-enriched single crystal sample, a QENS signal was clearly observed along the three principal axes [110], [111], and [001] at a temperature ($T$) of 600 K. Wave vector dependence of the linewidth of the QENS signal along each direction was explained well using the Chudley-Elliot model for jumps between the A sites of the perovskite lattice through the bottleneck square, which was also supported by molecular dynamics calculations. At $T=600$ K, the estimated self-diffusion coefficient of Li$^+$ ($D_{Li}$) in the $ab$ plane [$D^{ab}_{Li}=(6.8\pm0.5)\times 10^{-6}$ cm$^2$/s] was slightly larger than that along the $c$ axis [$D^{c}_{Li}=(4.4\pm0.3)\times 10^{-6}$ cm$^2$/s], suggesting quasi-isotropic diffusion, that is, the three-dimensional diffusion of Li$^+$ ions. The decrease in $D_{Li}$ with decreasing $T$ was reasonably explained by a thermal activation process with the activation energy determined from ionic-conductivity measurements. Furthermore, the estimated values of the self-diffusion coefficient of Li$^+$ ions are comparable to those in the sulfide-based Li$^+$ ion conductor, Li$_{7}$P$_{3}$S$_{11}$, although its ionic conductivity is 10 times larger than that for LLTO. The obtained microscopic information on Li$^+$ diffusion in LLTO clarifies how to understand the Li conduction mechanism in LLTO and Li$_{7}$P$_{3}$S$_{11}$ in a unified manner and can provide a way to increase the Li$^+$ ionic conductivity in oxide-based solid electrolytes.

Stable jump control for the wheel-legged robot based on TMS-DIP model

PurposeThe purpose of this paper is to propose a novel jump control method based on Two Mass Spring Damp Inverted Pendulum (TMS-DIP) model, which makes the third generation of hydraulic driven wheel-legged robot prototype (WLR-3P) achieve stable jumping.Design/methodology/approachFirst, according to the configuration of the WLR, a TMS-DIP model is proposed to simplify the dynamic model of the robot. Then the jumping process is divided into four stages: thrust, ascent, descent and compression, and each stage is modeled and solved independently based on TMS-DIP model. Through WLR-3P kinematics, the trajectory of the upper and lower centroids of the TMS-DIP model can be mapped to the joint space of the robot. The corresponding control strategies are proposed for jumping height, landing buffer, jumping attitude and robotic balance, so as to realize the stable jump control of the WLR.FindingsThe TMS-DIP model proposed in this paper can simplify the WLR dynamic model and provide a simple and effective tool for the jumping trajectory planning of the robot. The proposed approach is suitable for hydraulic WLR jumping control. The performance of the proposed wheel-legged jump method was verified by experiments on WLR-3P.Originality/valueThis work provides an effective model (TMS-DIP) for the jump control of WLR-3P. The results showed that the number of landing shock (twice) and the pitch angle fluctuation range (0.44 rad) of center of mass of the jump control method based on TMS-DIP model are smaller than those based on spring-loaded inverted pendulum model. Therefore, the TMS-DIP model makes the jumping process of WLR more stable and gentler.

Modeling and Optimization for Multi-Objective Nonidentical Parallel Machining Line Scheduling with a Jumping Process Operation Constraint

This paper investigates the nonidentical parallel production line scheduling problem derived from an axle housing machining workshop of an axle manufacturer. The characteristics of axle housing machining lines are analyzed, and a nonidentical parallel line scheduling model with a jumping process operation (NPPLS-JP), which considers mixed model production, machine eligibility constraints, and fuzzy due dates, is established so as to minimize the makespan and earliness/tardiness penalty cost. While the physical structures of the parallel lines in the NPPLS-JP model are symmetric, the production capacities and process capabilities are asymmetric for different models. Different from the general parallel line scheduling problem, NPPLS-JP allows for a job to transfer to another production line to complete the subsequent operations (i.e., jumping process operations), and the transfer is unidirectional. The significance of the NPPLS-JP model is that it meets the demands of multivariety mixed model production and makes full use of the capacities of parallel production lines. Aiming to solve the NPPLS-JP problem, we propose a hybrid algorithm named the multi-objective grey wolf optimizer based on decomposition (MOGWO/D). This new algorithm combines the GWO with the multi-objective evolutionary algorithm based on decomposition (MOEA/D) to balance the exploration and exploitation abilities of the original MOEA/D. Furthermore, coding and decoding rules are developed according to the features of the NPPLS-JP problem. To evaluate the effectiveness of the proposed MOGWO/D algorithm, a set of instances with different job scales, job types, and production scenarios is designed, and the results are compared with those of three other famous multi-objective optimization algorithms. The experimental results show that the proposed MOGWO/D algorithm exhibits superiority in most instances.

Feature selection in jump models

Jump models switch infrequently between states to fit a sequence of data while taking the ordering of the data into account We propose a new framework for joint feature selection, parameter and state-sequence estimation in jump models. Feature selection is necessary in high-dimensional settings where the number of features is large compared to the number of observations and the underlying states differ only with respect to a subset of the features. We develop and implement a coordinate descent algorithm that alternates between selecting the features and estimating the model parameters and state sequence, which scales to large data sets with large numbers of (noisy) features. We demonstrate the usefulness of the proposed framework by comparing it with a number of other methods on both simulated and real data in the form of financial returns, protein sequences, and text. By leveraging information embedded in the ordering of the data, the resulting sparse jump model outperforms all other methods considered and is remarkably robust to noise.

Dielectric permittivity and relaxation process investigation of C-doped TlInS2 crystals

In this work, permittivity of TlInS2 and TlInS2 (5%[Formula: see text]C) crystals has been investigated at the AC field with 100–500 K temperatures ranges. [Formula: see text] temperature dependencies of TlInS2 and TlInS2 (5%[Formula: see text]C) crystals are studied at the frequency range of 25–106 Hz. Activation energy and ionic conductivity nature of sample were analyzed. Moreover, permittivity of these samples was studied at the perpendicular direction of axis “[Formula: see text]” of TlInS2 (5%[Formula: see text]C) crystals. The jumping process in the TlInS2 (5%[Formula: see text]C) crystals was studied using [Formula: see text] temperature dependencies.