Network


Latest external collaboration on country level. Dive into details by clicking on the dots.

Hotspot


Dive into the research topics where W. Miczulski is active.

Publication


Featured researches published by W. Miczulski.


IEEE Transactions on Instrumentation and Measurement | 2013

Integrated System for Monitoring and Control of the National Time and Frequency Standard

J. Kaczmarek; W. Miczulski; M. Kozioł; Albin Czubla

In Poland, the national time and frequency standard (NTFS) is realized by means of three caesium atomic clocks, a system for the generation of the Polish time scale UTC(PL), and measuring systems for comparing directly and indirectly the atomic clocks and time scale. In order to ensure the best realization of NTFS, the Institute of Electrical Metrology at the University of Zielona Góra (IME UZ), in cooperation with the Central Office of Measures (GUM), has developed and built an integrated monitoring and control system. This system significantly increases the functional capabilities of autonomous measuring systems used previously at GUM and adds new features that increase the reliability and safety of the NTFS. Examples of such features are: automatic detection of invalid operating conditions (including breakdowns) of the atomic clocks and remote notification of alarms, as well as a new approach to predicting corrections for UTC(PL) using neural networks (NNs). In this paper, the presentation of hardware and software of the NTFS is limited to their general features. However, the application of NNs for predicting corrections for UTC(PL) and automation in determining the standard radio frequency deviation from its nominal value are presented in more detail.


IEEE Transactions on Instrumentation and Measurement | 2017

Algorithm for Predicting [UTC–UTC(k)] by Means of Neural Networks

W. Miczulski; Lukasz Sobolewski

This paper presents a new algorithm for predicting the deviations for local timescale UTC(k) in relation to the coordinated universal time (UTC) scale by means of group method of data handling (GMDH) neural network (NN). A very important element of this algorithm significantly affecting the quality of the obtained prediction is the block of time series preparation for NN. On the basis of carried out research for NN training and predicting the deviations, three time series (TS1, TS3, and TS4) are used. The values of elements of first time series (TS1) are calculated based on data from a cesium atomic clock realizing the UTC(k) timescale and on the deviations for this timescale determined by the French for Bureau International des Poids et Mesures (BIPM) in relation to the UTC timescale. The new time series (TS3) is a complement of time series TS1 by the deviations of the UTC(k) timescale in relation to the UTC Rapid timescale. The values of elements of next new time series (TS4) are equal to the deviations for UTC(k) in relation to the UTC and UTC Rapid timescales published by the BIPM. Simulation research of the algorithm has been carried out on the example of the UTC(PL) scale. The best results of [UTC–UTC(PL)] deviation predictions, compared with the previously used method of linear regression, designated with the uncertainty of 8 ns are obtained for GMDH NN and time series TS3.


Journal of Physics: Conference Series | 2016

Methods of time series preparation based on UTC and UTCr scales for predicting the [UTC-UTC(PL)]

Lukasz Sobolewski; W. Miczulski

The article presents the results of methods of preparation of two time series on the quality of predicting the [UTC-UTC(PL)] for the Polish Timescale UTC(PL) using GMDH neural networks. The first time series (TS1) was based on the [UTC-UTC(PL)] deviations designated by the BIPM. In the second time series (TS2) the deviations designated by the BIPM on the basis of the UTC and UTC Rapid scales were applied. The obtained results indicate that the use of time series for predicting the [UTC-UTC(PL)], based on deviations determined by the UTC and UTC Rapid scales, allowed to obtain more accurate predictions.


Metrology and Measurement Systems | 2012

Influence of the GMDH Neural Network Data Preparation Method on UTC(PL) Correction Prediction Results

W. Miczulski; Ł. Sobolewski


Bulletin of The Polish Academy of Sciences-technical Sciences | 2013

Prediction of corrections for the Polish time scale UTC(PL) using artificial neural networks

Marcel Luzar; Ł. Sobolewski; W. Miczulski; Józef Korbicz


Metrology and Measurement Systems | 2013

A NEW ELASTIC SCHEDULING TASK MODEL IN THE NODE OF A CONTROL AND MEASUREMENT SYSTEM

W. Miczulski; P. Powroźnik


Przegląd Elektrotechniczny | 2014

Dependence of mobile robot task scheduling on fitness functions

W. Miczulski; P. Powroźnik


Elektronika : konstrukcje, technologie, zastosowania | 2011

Application of the GMDH neural networks in prediction of corrections of the national time scale

W. Miczulski; Ł. Sobolewski


IEEE Transactions on Instrumentation and Measurement | 2018

A New Autocalibration Procedure in Intelligent Temperature Transducer

W. Miczulski; M. Krajewski; S. Sienkowski


Przegląd Elektrotechniczny | 2014

Inteligentne podstacje elektroenergetyczne

E. Michta; R. Szulim; A. Markowski; W. Miczulski

Collaboration


Dive into the W. Miczulski's collaboration.

Top Co-Authors

Avatar

P. Powroźnik

University of Zielona Góra

View shared research outputs
Top Co-Authors

Avatar

Ł. Sobolewski

University of Zielona Góra

View shared research outputs
Top Co-Authors

Avatar

Lukasz Sobolewski

University of Zielona Góra

View shared research outputs
Top Co-Authors

Avatar

A. Markowski

University of Zielona Góra

View shared research outputs
Top Co-Authors

Avatar

E. Michta

University of Zielona Góra

View shared research outputs
Top Co-Authors

Avatar

Dominik Sankowski

Lodz University of Technology

View shared research outputs
Top Co-Authors

Avatar

J. Kaczmarek

University of Zielona Góra

View shared research outputs
Top Co-Authors

Avatar

Józef Korbicz

University of Zielona Góra

View shared research outputs
Top Co-Authors

Avatar

M. Kozioł

University of Zielona Góra

View shared research outputs
Top Co-Authors

Avatar

M. Krajewski

University of Zielona Góra

View shared research outputs
Researchain Logo
Decentralizing Knowledge