Maria Vircikova

Design of LCL Filter Using Hybrid Intelligent Optimization for Photovoltaic System

This paper proposes new design method of LCL filter parameters by using hybrid intelligent optimization. Usually, the voltage-source inverters (VSI) have been used for control of both link voltage and power factor in the grid connected topologies. But, since VSI can cause high-order harmonics by high switching frequency, it is important to choose the filter parameters to achieve good filtering effect. Compared with traditional L and LC filter, LCL filter is known as an effective method on reducing harmonic distortion at the switching frequency of VSI. However, design of the LCL filter is complex, and the selection of the initial values of the inductors is difficult at the start of the design process. This paper proposes an approach for designing LCL filter parameters by hybrid optimization method with both genetic algorithm and clonal selection. Simulation results are provided to prove that the proposed method is more effective and simple than conventional methods.

Experience with the Children-Humanoid Interaction in Rehabilitation Therapy for Spinal Disorders

This paper deals with the social human-robot interaction with children towards the application outside tightly controlled and constrained laboratory environments. We present our experience of using the Nao humanoid robotic platform in a role of a physiotherapist for rehabilitation and prevention of scoliosis in a children’s hospital and an elementary school in Slovakia. Before and between the exercise units, children could freely interact with a robot, what contributed to building-up robot-children relationship. The results indicate the potential of humanoids to enhance the quality of life of children in an entertaining and effective way.

Design of verbal and non-verbal Human-Robot Interactive System

This paper describes new ways to interface humans and multi-robot systems in an effective way. We present a project developed in an effort to create a Human-Robot Interactive System where a user freely cooperates with robots and in such way the robot learns to recognize his verbal and non-verbal exposes. Humans are involved into the process of the adaptation of humanoid performance, shown on the example of teaching humanoids an aesthetic motion. Due to the methods used, this approach is useful for personalization of robotic behaviors in accordance to the users preference.

Neural approach for personalised emotional model in human-robot interaction

Robotic technology is widely spreading into our everyday lives so the social interaction between robots and humans is becoming more important. We believe that the quality of human-robot interaction will determine the effectiveness of the collaboration and in general, the acceptance of robots in the society. With this motivation we propose a more intuitive way of interacting with robotic companions by recognizing human emotions and, on the other side, a methodology to construct a comprehensible way of expressing internal states of machines for human users. The novelty of our model consists in personalization of the emotional expressions for each of the users. The system consists of two parts: learning emotional expressions of humans using ARTMAP neural network and implementation of the personalised emotional model to a humanoid robot. This paper shows the first results based on experiments realized on the humanoid platform Nao. We achieved a personalised emotional behaviour especially useful in the area of social robotics.

Socially-Assistive Emotional Robot that Learns from the Wizard During the Interaction for Preventing Low Back Pain in Children

Back pain causes more global disability than any other health problem studied and the number of patients is growing. In Europe and in the US it is the number one cause of lost work days. This paper propounds a new approach by exploring the effect of utilizing a humanoid robot as a therapy-assistive tool in educating children to perform back exercises designed by a professional therapist. In our previous research a NAO robot was programmed and employed as a robotic assistant to a human physiotherapist to perform exercises in an elementary school in Slovakia. This paper goes further in designing a Wizard of Oz, where the exercises can be controlled and intervened by motivational behaviors of the robot (emotional expressions). Currently we are developing a system based on reinforcement learning that should adopt the motivational interventions from the Wizard. The promising results of this study in the physical therapy suggest the effective future use of social robots in reducing the symptoms of the most extended global disability in the world.

Artificial Intelligence in Public Health Prevention of Legionelosis in Drinking Water Systems

Good quality water supplies and safe sanitation in urban areas are a big challenge for governments throughout the world. Providing adequate water quality is a basic requirement for our lives. The colony forming units of the bacterium Legionella pneumophila in potable water represent a big problem which cannot be overlooked for health protection reasons. We analysed several methods to program a virtual hot water tank with AI (artificial intelligence) tools including neuro-fuzzy systems as a precaution against legionelosis. The main goal of this paper is to present research which simulates the temperature profile in the water tank. This research presents a tool for a water management system to simulate conditions which are able to prevent legionelosis outbreaks in a water system. The challenge is to create a virtual water tank simulator including the water environment which can simulate a situation which is common in building water distribution systems. The key feature of the presented system is its adaptation to any hot water tank. While respecting the basic parameters of hot water, a water supplier and building maintainer are required to ensure the predefined quality and water temperature at each sampling site and avoid the growth of Legionella. The presented system is one small contribution how to overcome a situation when legionelosis could find good conditions to spread and jeopardize human lives.

Adaptive Fuzzy Cognitive Maps Using Interactive Evolution: A Robust Solution for Navigation of Robots

Fuzzy cognitive maps belong to emerging approaches used for various tasks in artificial intelligence. They are especially useful for solving the problem of navigation of vehicles as fuzzy systems are very robust in general. Therefore, they are suitable for the real world applications. One of disadvantages of fuzzy systems is their inability to learn. In this paper, we propose the use of fuzzy cognitive maps for navigation of a humanoid robot Nao and also an adaptive mechanism based on interactive evolution. To get data about the surrounding world, we are using the robot’s camera. Depending on the situation in the arena, the best direction is selected with the use of membership functions for target and obstacles. Parameters of these functions can be set manually from a program interface or the optimal parameters can be found using interactive evolution. The interactive evolution was selected to obtain the best results in the shortest time. Two approaches to the interactive evolution were tested. The first type was a simple interactive evolution, the second type used thresholds to find the most promising individuals to hold the ideal parameters and only these were presented to a human for evaluation. Experiments were made using manual setting of the parameters as well as using the adaptation mechanism of the first and the second type, where the second type was able to find the right set of parameters in a shorter time than the first one.

Discovering Art in Robotic Motion: From Imitation to Innovation via Interactive Evolution

The proposed research deals with a usage of artificial intelligent techniques in humanoid robotics. The objective is to design an interactive robotic behavior. The focus is on social robotics and how to improve the interaction between human and robot. In the first part user interactively cooperates with the System and this is the way how the robot acquires knowledge. Our approach is based on real manipulation where the robot freely interacts with humans and objects, and learns during continuous interaction with the environment. The experimental setup is the humanoid robot NAO. In the second part of the system we use the Interactive Evolutionary Computation – a technique which optimizes systems based on subjective human evaluation. So far, the algorithm is applied to a system of evolution of pleasant motions or dances. A user can build a robot behavior without any programming knowledge and then the evolutionary algorithm helps user to discover new possibilities of motions.

Interactive Q-learning for social robots that learn from the Wizard: A pilot study

This paper discusses interactive Q-learning as a method for learning appropriate robot behavior from the human teleoperator in order to increase the robots autonomy in various human-robot interaction scenarios. We demonstrate the usability of our approach on a simulation of a simple task. The results show the feasibility of this method in different kinds of interactions. As a conclusion, we discuss the results and the next steps of the research.

A Method to Evaluate Intuitive Sense by Using a Robotic Tool: Towards Engineering for Assistive Technology and Accessibility

The evaluation of subjective feelings of discomfort is a serious problem in the field of ergonomics, environment engineering and human-robot interactions. It is difficult to measure them by using objective quantifications in a traditional sense. We have hypothesized that the unpleasant feeling emerges caused by deviating from the autonomy, which exists in the perception-action cycle, the convective flow in two layers of intuition and perception and continuity in the dialogue. The present paper discusses the issue with respect to two examples; an unexpected breaking of balance in the trained motion and irritation at the lack of progress by discontinuity in the dialogue, and attempts to build a theoretical framework to detect a deviation from ongoing autonomy. This paper discusses a way of the theoretical modeling and explores possible implementations into the robotic experiment that reproduces human behaviors in need of supervision by the central nervous system. This preliminary report can contribute to an extension of the coupling between the central and peripheral nervous systems to social communications which is supported by assistive technologies and accessibility improvements.