Роботизований комплекс точкового оброблення гербіцидами сільськогосподарських угідь

В роботі отримано нове вирішення актуальної науково-практичної задачі підвищення ефективності сільськогосподарського виробництва шляхом розроблення роботизованого комплексу точкового оброблення гербіцидами сільськогосподарських угідь, розроблено математичну модель системи розпізнавання рослин, спроектовано логічно-фізичну систему робота. Значну увагу в роботі приділено моделюванню системи розпізнавання паростків рослин за допомогою програмного комплексу Matlab/Simulink.. Результати цього моделювання підтвердили правильність розробленої системи розпізнавання.The paper presents a new solution to the actual scientific and practical problem of increasing the efficiency of agricultural production by developing a robotic complex of point cultivation with herbicides, developed a mathematical model of the system of plant recognition, designed a logical and physical system of work. Much attention is paid to the modeling of plant sprout recognition system using Matlab/Simulink software. The results of this simulation confirmed the correctness of the developed recognition system

Analytische Maschinen

In dieser Arbeit präsentieren wir einige Resultate über analytische Maschinen hinsichtlich des Berechenbarkeitsbegriffs über Q und R, der Lösungen von Differentialgleichungen und des Stabilitätsproblems dynamischer Systeme. Wir erläutern zuerst das Maschinenmodell, das eine Art von BLUM-SHUB-SMALE Maschine darstellt, erweitert um unendliche, konvergente Berechnungen. Danach vergleichen wir die Mächtigkeit dieses Berechnungsmodells über den Körpern Q und R und zeigen z.B., daß endliche Berechnungen mit reellen Zahlen durch unendliche, konvergente Berechnungen mit rationalen Zahlen simuliert werden können, wobei die Genauigkeit der Approximation während des Prozesses nicht bekannt ist. Analytische Berechnungen über R sind echt mächtiger als über Q. Unsere Aufmerksamkeit wendet sich dann gewöhnlichen Differentialgleichungen (DGl) zu, bei denen wir hinreichende Kriterien für die Berechenbarkeit von Lösungen innerhalb unseres Modells angeben. Schließlich untersuchen wir dynamische Systeme, die durch DGl beschrieben werden, und zeigen die Unentscheidbarkeit einer Klasse von Stabilitätsproblemen für dynamische Systeme.In this thesis we present some results about analytic machines regarding computability over Q and R, solutions of differential equations, and the stability problem of dynamical systems. We first explain the machine model, which is a kind of BLUM-SHUB-SMALE machine enhanced by infinite convergent computations. Next, we compare the computational power of such machines over the fields Q and R showing e.g. that finite computations with real numbers can be simulated by infinite converging computations on rational numbers, but the precision of the approximation is not known during the process. Analytic computations over R are strictly more powerful than over Q. Our attention is then shifted to ordinary differential equations (ODEs) where we establish sufficient criteria for the computability of their solutions within our model. We investigate dynamical systems described by ODEs and show the undecidability of a class of stability problems for dynamical systems

Theoretical Deep Learning

Deep learning has long been criticised as a black-box model for lacking sound theoretical explanation. During the PhD course, I explore and establish theoretical foundations for deep learning. In this thesis, I present my contributions positioned upon existing literature: (1) analysing the generalizability of the neural networks with residual connections via complexity and capacity-based hypothesis complexity measures; (2) modeling stochastic gradient descent (SGD) by stochastic differential equations (SDEs) and their dynamics, and further characterizing the generalizability of deep learning; (3) understanding the geometrical structures of the loss landscape that drives the trajectories of the dynamic systems, which sheds light in reconciling the over-representation and excellent generalizability of deep learning; and (4) discovering the interplay between generalization, privacy preservation, and adversarial robustness, which have seen rising concerns in deep learning deployment

Bounds for the Computational Power and Learning Complexity of Analog Neural Nets (Extended Abstract)

) Wolfgang Maass* Institute for Theoretical Computer Science Technische Universitaet Graz Klosterwiesgasse 32/2 A-8010 Graz, Austria e-mail: [email protected] October 23, 1992 Abstract It is shown that feedforward neural nets of constant depth with piecewise polynomial activation functions and arbitrary real weights can be simulated for boolean inputs and outputs by neural nets of a somewhat larger size and depth with heaviside gates and weights from f0; 1g. This provides the first known upper bound for the computational power and VC-dimension of such neural nets. It is also shown that in the case of piecewise linear activation functions one can replace arbitrary real weights by rational numbers with polynomially many bits, without changing the boolean function that is computed by the neural net. In addition we improve the best known lower bound for the VC-dimension of a neural net with w weights and gates that use the heaviside function (or other common activation functions suc..