Wrocław University of Technology

About: Wrocław University of Technology is a education organization based out in Wrocław, Poland. It is known for research contribution in the topics: Laser & Computer science. The organization has 13115 authors who have published 31279 publications receiving 338694 citations.

Self-healing coatings in anti-corrosion applications

Abstract: Nonmetallic (based on polymers or oxides) and metallic protective coatings are used to protect metal products against the harmful action of the corrosion environment. In recent years, self-healing coatings have been the subject of increasing interest. The ability of such coatings to self-repair local damage caused by external factors is a major factor contributing to their attractiveness. Polymer layers, silica-organic layers, conversion layers, metallic layers and ceramic layers, to mention but a few, are used as self-healing coatings. This paper presents the main kinds of self-healing coatings and explains their self-healing mechanisms.

Performance Improvement of Industrial Drives With Mechanical Elasticity Using Nonlinear Adaptive Kalman Filter

Abstract: This paper deals with the application of the adaptive control structure for torsional vibration suppression in the drive system with an elastic coupling The proportional-integral speed controller and gain factors of two additional feedback loops, from the shaft torque and load side speed, are tuned on-line according to the changeable load side inertia This parameter, as well as other mechanical variables of the drive system (load side speed, torsional and load torques), are estimated with the use of the developed nonlinear extended Kalman filter (NEKF) The initial values of the Kalman filter covariance matrices are set using the genetic algorithm Then, to ensure the smallest state and parameter estimation errors, the on-line adaptation law for the chosen element of the state covariance matrix of the NEKF is proposed The described control strategy is tested in an open and a closed-loop control structure The simulation results are confirmed by laboratory experiments

Matching Organizational Structure and Social Network Extracted from Email Communication

Abstract: The following paper presents the concept of matching social network and corporate hierarchy in organizations with stable corporate structure. The idea allows to confirm whether social position of an employee calculated on the basis of the social network differs significantly from the formal employee role in the company. The results of such analysis may lead to possible company management improvement enabling to gain a competitive edge. In order to perform this task the authors have made experiments with the use of two real-life datasets: Enron and mid-sized manufacturing companies showing which social network metrics may be suitable to match organizational structure and social network with good results.

GED: the method for group evolution discovery in social networks

Abstract: The continuous interest in the social network area contributes to the fast development of this field. The new possibilities of obtaining and storing data facilitate deeper analysis of the entire network, extracted social groups and single individuals as well. One of the most interesting research topic is the dynamics of social groups, it means analysis of group evolution over time. Having appropriate knowledge and methods for dynamic analysis, one may attempt to predict the future of the group, and then manage it properly in order to achieve or change this predicted future according to specific needs. Such ability would be a powerful tool in the hands of human resource managers, personnel recruitment, marketing, etc. The social group evolution consists of individual events and seven types of such changes have been identified in the paper: continuing, shrinking, growing, splitting, merging, dissolving and forming. To enable the analysis of group evolution a change indicator - inclusion measure was proposed. It has been used in a new method for exploring the evolution of social groups, called Group Evolution Discovery (GED). The experimental results of its use together with the comparison to two well-known algorithms in terms of accuracy, execution time, flexibility and ease of implementation are also described in the paper.