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.
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.
The social foraging behavior of Escherichia coli bacteria has recently been studied by several researchers to develop a new algorithm for distributed optimization control. The Bacterial Foraging Optimization Algorithm (BFOA), as it is called now, has many features analogous to classical Evolutionary Algorithms (EA). Passino [1] pointed out that the foraging algorithms can be integrated in the framework of evolutionary algorithms. In this way BFOA can be used to model some key survival activities of the population, which is evolving. This article proposes a hybridization of BFOA with another very popular optimization technique of current interest called Differential Evolution (DE). The computational chemotaxis of BFOA, which may also be viewed as a stochastic gradient search, has been coupled with DE type mutation and crossing over of the optimization agents. This leads to the new hybrid algorithm, which has been shown to overcome the problems of slow and premature convergence of both the classical DE and BFOA over several benchmark functions as well as real world optimization problems.
A partial order on a bounded lattice L is called t-order if it is defined by means of the t-norm on L. It is obtained that for a t-norm on a bounded lattice L the relation a⪯Tb iff a=T(x,b) for some x∈L is a partial order. The goal of the paper is to determine some conditions such that the new partial order induces a bounded lattice on the subset of all idempotent elements of L and a complete lattice on the subset A of all elements of L which are the supremum of a subset of atoms.
In this paper a fuzzy relation-based framework is shown to be suitable to describe not only knowledge-based medical systems, explicitly using fuzzy approaches, but other ways of knowledge representation and processing. A particular example, the practically tested medical expert system Disco, is investigated from this point of view. The system is described in the fuzzy relation-based framework and compared with CADIAG-II-like systems that are a "pattern" for computer-assisted diagnosis systems based on a fuzzy technology. Similarities and discrepancies in - representation of knowledge, patient's information, inference mechanism and interpretation of results (diagnoses) - of the systems are established. This work can be considered as another step towards a general framework for computer-assisted medical diagnosis.
Lake Lednica, Greater Poland, is one of Poland’s most important and longest-studied underwater archaeological sites. The residential centre established on an island was one of the central points in the state of the first Piasts. Previous research located two bridges to the island and discovered the largest collection of early medieval military objects in Central Europe in the lake. In the 2017 season, a third bridge was discovered on Lake Lednica leading to the small island called Ledniczka on which the layers of an early medieval settlement and clear remnants of a motte-type medieval structure are found. Three seasons of research on relics of the crossing suggest that it may have functioned in two periods: in the tenth century and at the turn of the fourteenth century. During the research, a number of military items, pottery, objects made of organic materials and fishing tools were found. and Lednické jezero ve Velkopolsku patří k nejdůležitějším a nejdéle studovaným lokalitám podvodní archeologie ve střední Evropě. Rezidenční centrum zřízené na zdejším Lednickém ostrově bylo jedním z hlavních míst prvních Piastovců. Předchozí výzkum odhalil dva mosty spojující ostrov s pevninou a největší soubor militárií ze dna středoevropského jezera. V roce 2017 byl identifikován další most, tentokrát zpřístupňující ostrůvek zvaný Ledniczka. Další výzkum ukázal, že most pravděpodobně fungoval ve dvou periodách: v 10. století a na přelomu 13. a 14. století. Přinesl také početný soubor militárií, keramiky, rybářského náčiní a výrobků z organických materiálů. Na ostrůvku byly dokumentovány raně středověké situace a opevnění typu motte.