In a Discounted Markov Decision Process (DMDP) with finite action sets the Value Iteration Algorithm, under suitable conditions, leads to an optimal policy in a finite number of steps. Determining an upper bound on the necessary number of steps till gaining convergence is an issue of great theoretical and practical interest as it would provide a computationally feasible stopping rule for value iteration as an algorithm for finding an optimal policy. In this paper we find such a bound depending only on structural properties of the Markov Decision Process, under mild standard conditions and an additional "individuality" condition, which is of interest in its own. It should be mentioned that other authors find such kind of constants using non-structural information, i.e., information not immediately apparent from the Decision Process itself. The DMDP is required to fulfill an ergodicity condition and the corresponding ergodicity index plays a critical role in the upper bound.
In this paper we obtain a strong invariance principle for negatively associated random fields, under the assumptions that the field has a finite $(2+\delta )$th moment and the covariance coefficient $u(n)$ exponentially decreases to $0$. The main tools are the Berkes-Morrow multi-parameter blocking technique and the Csörgő-Révész quantile transform method.
Let F be a class of entire functions represented by Dirichlet series with complex frequencies ∑ ake hλ k ,zi for which (|λ k |/e)|λ k | k!|ak| is bounded. Then F is proved to be a commutative Banach algebra with identity and it fails to become a division algebra. F is also proved to be a total set. Conditions for the existence of inverse, topological zero divisor and continuous linear functional for any element belonging to F have also been established.
In this paper, we study decentralized H∞ feedback control systems with quantized signals in local input-output (control) channels. We first assume that a decentralized output feedback controller has been designed for a multi-channel continuous-time system so that the closed-loop system is Hurwitz stable and a desired H∞ disturbance attenuation level is achieved. However, since the local measurement outputs are quantized by a general quantizer before they are passed to the controller, the system's performance is not guaranteed. For this reason, we propose a local-output-dependent strategy for updating the quantizers' parameters, so that the closed-loop system is asymptotically stable and achieves the same H∞ disturbance attenuation level. We also extend the discussion and the result to the case of multi-channel discrete-time H∞ feedback control systems.
In this report, a control method for the stabilization of periodic orbits for a class of one- and two-dimensional discrete-time systems that are topologically conjugate to symbolic dynamical systems is proposed and applied to a population model in an ecosystem and the Smale horseshoe map. A periodic orbit is assigned as a target by giving a sequence in which symbols have periodicity. As a consequence, it is shown that any periodic orbits can be globally stabilized by using arbitrarily small control inputs. This work is a new attempt to systematically design a control system based on symbolic dynamics in the sense that one estimates the magnitude of control inputs and analyzes the Lyapunov stability.
Adiposis is reputed as a twin disease of type 2 diabetes and
greatly harmful to human health. In order to understand the
molecular mechanisms of adiposis, the changes of physiological,
pathological, epigenetic and correlative gene expression were
investigated during the adiposis development of C57BL/6J mice
induced by long time (9 months) high-fat and high-sucrose diet
(HFSD) sustainably. The results showed that mRNA transcription
level of the Leptin, Glut4 and Glut2 genes have been obviously
changed, which exhibit a negative correlation with methylation
on their promoter DNA. The results also revealed that HFSD
induced higher level of DNA methyltransferase 1 (DNMT1) in fat
tissue might play important role in regulating the changes of
methylation pattern on Glut4 and Leptin genes, and which might
be one of the molecular mechanisms for the adiposis
development.
A dependence measure for arbitrary type pairs of random variables is proposed and analyzed, which in the particular case where both random variables are continuous turns out to be a concordance measure. Also, a sample version of the proposed dependence measure based on the empirical subcopula is provided, along with an R package to perform the corresponding calculations.
Oxidative stress is an imbalance between free radicals and antioxidants, and is an important etiological factor in the development of hypertension. Recent experimental evidence suggests that subpressor doses of angiotensin II elevate oxidative stress and blood pressure. We aimed to investigate the oxidative stress related mechanism by which a subpressor dose of angiotensin II induces hypertension in a normotensive rat model. Normotensive male Wistar rats were infused with a subpressor dose of angiotensin II for 28 days. The control group was sham operated and infused with saline only. Plasma angiotensin II and H2O2 levels, whole-blood glutathione peroxidase, and AT-1a, Cu/Zn SOD, and p22phox mRNA expression in the aorta was assessed. Systolic and diastolic blood pressures were elevated in the experimental group. There was no change in angiotensin II levels, but a significant increase in AT-1a mRNA expression was found in the experimental group. mRNA expression of p22phox was increased significantly and Cu/Zn SOD decreased significantly in the experimental group. There was no significant change to the H2O2 and GPx levels. Angiotensin II manipulates the free radical-antioxidant balance in the vasculature by selectively increasing O2 - production and decreasing SOD activity and causes an oxidative stress induced elevation in blood pressure in the Wistar rat., M. M. Govender, A. Nadar., and Obsahuje bibliografii
Swarm intelligence is an emerging field with wide-reaching application opportunities in problems of optimization, analysis and machine learning. While swarm systems have proved very effective when applied to a variety of problems, swarm-based methods for computer vision have received little attention. This paper proposes a swarm system capable of extracting and exploiting the geometric properties of objects in images for fast and accurate recognition. In this approach, computational agents move over an image and affix themselves to relevant features, such as edges and corners. The resulting feature profile is then processed by a classification subsystem to categorize the object. The system has been tested with images containing several simple geometric shapes at a variety of noise levels, and evaluated based upon the accuracy of the system's predictions. The swarm system is able to accurately classify shapes even with high image noise levels, proving this approach to object recognition to be robust and reliable.
This paper considers the problem of swinging up the Furuta pendulum and proposes a new smooth nonlinear swing up controller based on the concept of energy. This new controller results from the Total Energy Control System (TECS) approach in conjunction with a linearizing feedback controller. The new controller commands to the desired reference the total energy rate of the Furuta pendulum; thus, the Furuta pendulum oscillates and reaches a neighborhood of its unstable configuration while the rotation of its base remains bounded. Once the Furuta pendulum configuration is in the neighborhood of its unstable equilibrium point, a linear controller stabilizes the unstable configuration of the Furuta pendulum. Real-time experiments are included to support the theoretical developments.