Architected lattices for simultaneous broadband attenuation of airborne sound and mechanical vibrations in all directions

Phononic crystals and acoustic metamaterials are architected lattices designed to control the propagation of acoustic or elastic waves. In these materials, the dispersion properties and the energy transfer are controlled by selecting the lattices' geometry and their constitutive material properties. Most designs, however, only affect one mode of energy propagation, transmitted either as acoustic, airborne sound or as elastic, structural vibrations. Here, we present a design methodology to attenuate both acoustic and elastic waves simultaneously in all polarizations. We experimentally realize the first three-dimensional, load bearing, architected lattice, composed of a single-material, that responds in a broadband frequency range in all directions

Implementation of Trajectory Planning Algorithms for Track Serving Mobile Robot in ROS 2 Ecosystem

In this paper, our goal is to present the autonomous cone placing robot developed at SzéchenyiIstván University (Győr, Hungary) and the main steps and parts of its design and preparation. Within this, rather complex task-sequence, we discuss the logic of software operation (embedded in the ROS 2 ecosystem), the main issues of environmental representation and we focus especially on the trajectory planner part of the entire system. The implemented algorithms-including our own innovative ideas are Dijkstra, A*, Hybrid A*, DWA and Elastic band

Robot Cooperation without Explicit Communication by Fuzzy Signatures and Decision Trees

This paper presents a novel action selection method for multi robot task sharing problem. Two autonomous mobile robots try to cooperate for push a box to a goal position. Both robots equipped with object and goal sensing, but do not have explicit communication ability. We explore the use of fuzzy signatures and decision making system to intention guessing and efficient action selection. Virtual reality simulation is used to build and test our proposed algorithm

Capsule Networks for Object Segmentation Using Virtual World Dataset

The classical convolutional neural networks performance looks exceptionally great when the test dataset are very close to the training dataset. But when it is not possible, the accuracy of neural networks may even be reduced. The capsule networks are trying to solve the problems of the classical neural networks. Capsule networks are a brand new type of artificial neural networks, introduced by Geoffrey Hinton and his research team. In this work we would like to training capsule based neural networks for segmentation tasks, when the training set and test set are very different. For the training we use only computer generated virtual data, and we test our networks on real world data. We created three different capsule based architectures, based on classical neural network architectures, such as U-Net, PSP Net and ResNet. Experiences show how capsule networks are efficient in this special case

Számítási intelligencia algoritmusok, rendszerek és modellek = Algorithms, systems and models in computational intelligence

Korábbi eredményeinkre építve javasoltuk egy evolúciós (pl. bakteriális, részecskeraj) memetikus algoritmuscsaládot, az LM, maxi gradiens, és kombinációs eljárásokat alkalmaztunk lokális keresésre. Az új módszerek jobb konvergenciasebességgel és –pontossággal rendelkeznek, különösen a fuzzy modellek konstrukciójában. Javaslatot tettünk multipopulációs, többszálas és hibrid evolúciós, iteratív mohó és ütemezett vegyes evolúciós és memetikus eljárásokra. Szabványos adathalmazokon e módszerekkel az eddig publikált eredményeknél jobbat értünk el. Vizsgáltuk a fuzzy neurális hálózatokat, új struktúrákat, műveleteket bevezetve megkezdtük a hardver implementációt; fuzzy kognitív térképeket vizsgáltunk. Javaslatot tettünk a fuzzy szignatúrák geometriailag struktúrált általánosítására, változó finomságú szituációs térképek leírására. Javasoltuk a fuzzy 2 dimenziós raszterek alkalmazását a képreprezentációban. Az új komplex fuzzy - evolúciós/mohó/gradiens alapú optimalizációs - neurális hálózat eszközkészletet a műszaki és alkalmazott problémák széles körében használtuk fel, így a távközlési, a szállítási és logisztikai hálózatok optimalizációjára, hibadetektálásra; intelligens mobil robotok irányítására, kommunikációjára és autonóm együttműködésére; ellátási láncok és gyártási folyamatok optimalizálására; erőforrásallokációra és –ütemezésre; karakterfelismerésre és az építő- és környezetmérnöki döntéstámogatásra. | Based on our earlier research results we proposed a family of enhanced bacterial and evolutionary other memetic algorithms (e.g. Partical Swarm Optimization), with Levenberg-Marquard and Steepest Descent, viz. combinatorial methods for local search. The new methods have better convergence speed and accuracy, especially in fuzzy rule based model construction. We proposed multipopulation, multithread and hybrid evolutionary, iterative greedy and alternatingly scheduled mixed evolutionary and memetic approaches. We have achieved better results for standard benchmark data sets than any other authors. We studied neural networks based on fuzzy operations, proposing new structures, new operation families and starting hardware implementation; and we simulated fuzzy cognitive maps. We proposed extended classes of fuzzy signatures with geometric structure, modeling situational maps with flexible depth and fineness. We proposed fuzzy 2D grids for image representation. The new complex fuzzy - evolutionary/greedy/gradient optimization - neural network tool kit thus developed was deployed for a wide variety of engineering and applied problems, telecommunication, transport and logistic network optimization and failure detection, intelligent and mobile robot control, communication and co-ordination of autonomous collaboration; optimization of supply chains and production, resource allocation and scheduling, character recognition, and decision support in civil and environmental engineering