83 research outputs found
Partial Information Differential Games for Mean-Field SDEs
This paper is concerned with non-zero sum differential games of mean-field
stochastic differential equations with partial information and convex control
domain. First, applying the classical convex variations, we obtain stochastic
maximum principle for Nash equilibrium points. Subsequently, under additional
assumptions, verification theorem for Nash equilibrium points is also derived.
Finally, as an application, a linear quadratic example is discussed. The unique
Nash equilibrium point is represented in a feedback form of not only the
optimal filtering but also expected value of the system state, throughout the
solutions of the Riccati equations.Comment: 7 page
Fully coupled forward-backward stochastic differential equations driven by sub-diffusions
In this paper, we establish the existence and uniqueness of fully coupled
forward-backward stochastic differential equations (FBSDEs in short) driven by
anomalous sub-diffusions under suitable monotonicity conditions on
the coefficients. Here is a Brownian motion on and , is the inverse of a subordinator with drift
that is independent of . Various a priori estimates on the
solutions of the FBSDEs are also presented
A PRELIMINARY STUDY OF ROOT-TO-SHOOT REGENERATION BY ECTOPIC EXPRESSION OF WUS IN ARABIDOPSIS THALIANA ROOTS
Master'sMASTER OF SCIENC
Underwater dual manipulators-Part II: Kinematics analysis and numerical simulation
1104-1112This paper introduces dual-arm underwater manipulators mounted on an autonomous underwater vehicle (AUV), which can accomplish the underwater handling task. Firstly, the mechanical structure of the dual-arm system is briefly introduced, wherein each 4-DOF manipulator has an additional grasping function. In addition, the kinematics model of the manipulator is derived by using the improved D-H method. Secondly, the working space of the underwater dual-arm system is analyzed, which is obtained by using Monte Carlo method. The cubic polynomial interpolation and the five polynomial interpolation trajectory planning methods are compared in the joint space. Finally, with the help of the Robotics Toolbox software, the numerical test is conducted to verify the functions of the underwater dual-arm manipulator system
Optimal Dividend Payments for the Piecewise-Deterministic Poisson Risk Model
This paper considers the optimal dividend payment problem in
piecewise-deterministic compound Poisson risk models. The objective is to
maximize the expected discounted dividend payout up to the time of ruin. We
provide a comparative study in this general framework of both restricted and
unrestricted payment schemes, which were only previously treated separately in
certain special cases of risk models in the literature. In the case of
restricted payment scheme, the value function is shown to be a classical
solution of the corresponding HJB equation, which in turn leads to an optimal
restricted payment policy known as the threshold strategy. In the case of
unrestricted payment scheme, by solving the associated integro-differential
quasi-variational inequality, we obtain the value function as well as an
optimal unrestricted dividend payment scheme known as the barrier strategy.
When claim sizes are exponentially distributed, we provide easily verifiable
conditions under which the threshold and barrier strategies are optimal
restricted and unrestricted dividend payment policies, respectively. The main
results are illustrated with several examples, including a new example
concerning regressive growth rates.Comment: Key Words: Piecewise-deterministic compound Poisson model, optimal
stochastic control, HJB equation, quasi-variational inequality, threshold
strategy, barrier strateg
Numerical study of the fluid fracturing mechanism of granite at the mineral grain scale
Hydraulic fracturing is an essential technique for reservoir stimulation in the process of deep energy exploitation. Granite is composed of different rock-forming minerals and exhibits obvious heterogeneity at the mesoscale, which affects the strength and deformation characteristics of rocks and controls the damage and failure processes. Therefore, in this paper, based on the discrete element fluid-solid coupling algorithm and multiple parallel bond-grain based model (Multi Pb-GBM), a numerical model of a granite hydraulic fracturing test is established to study the evolution of hydraulic fractures in crystalline granite under different ground stress conditions. The main conclusions are as follows. The crack propagation of hydraulic fractures in granite is determined by the in situ stress state, crystal size, and mineral distribution, and the ground stress is the main controlling factor. The final fracture mode affects the maximum principal stress and shear stress, and the generation of cracks changes the distribution of the stress field. The hydraulic fracturing initiation pressure decreases with decreasing crystal size. The influence of the crystal size on the crack inclination angle is mainly reflected in local areas, and the general trend of the fissure dip angle distribution is along the direction of the maximum in situ stress. This study not only has important theoretical significance for clarifying the propagation mechanism of hydraulic fractures but also provides a theoretical basis for deep reservoir reconstruction and energy extraction
Boosting Adversarial Attacks by Leveraging Decision Boundary Information
Due to the gap between a substitute model and a victim model, the
gradient-based noise generated from a substitute model may have low
transferability for a victim model since their gradients are different.
Inspired by the fact that the decision boundaries of different models do not
differ much, we conduct experiments and discover that the gradients of
different models are more similar on the decision boundary than in the original
position. Moreover, since the decision boundary in the vicinity of an input
image is flat along most directions, we conjecture that the boundary gradients
can help find an effective direction to cross the decision boundary of the
victim models. Based on it, we propose a Boundary Fitting Attack to improve
transferability. Specifically, we introduce a method to obtain a set of
boundary points and leverage the gradient information of these points to update
the adversarial examples. Notably, our method can be combined with existing
gradient-based methods. Extensive experiments prove the effectiveness of our
method, i.e., improving the success rate by 5.6% against normally trained CNNs
and 14.9% against defense CNNs on average compared to state-of-the-art
transfer-based attacks. Further we compare transformers with CNNs, the results
indicate that transformers are more robust than CNNs. However, our method still
outperforms existing methods when attacking transformers. Specifically, when
using CNNs as substitute models, our method obtains an average attack success
rate of 58.2%, which is 10.8% higher than other state-of-the-art transfer-based
attacks
- …