Classification of LPG clients using the Hurst exponent and the correlation coeficient

In this paper we present the analysis of the gas usage for different types of buildings. First, we introduce the classical theory of building heating. This allows the establishment of theoretical relations between gas consumption time series and the outside air temperature for different types of buildings, residential and industrial. These relations imply dierent auto-correlations of gas usage time series as well as different cross-correlations between gas consumption and temperature time series for different types of buildings. Therefore, the autocorrelation and the cross-correlation were used to classify the buildings into three classes: housing, housing with high thermal capacity, and industry. The Hurst exponent was calculated using the global DFA to investigate auto-correlation, while the Kendall's τ rank coeficient was calculated to investigate cross-correlation

Detection of emergent leaks using machine learning approaches

In this work, we focus on the detection of leaks occurring in district metered areas (DMAs). Those leaks are observable as a number of time-related deviations from zone patterns over days or weeks. While they are detectable given enough time, due to the huge cost of water loss resulting from an undetected leak, the main challenge is to find them as soon as possible, when the deviation from the zone pattern is small. Using our collected observational data, we investigate the appearance of leaks and discuss the performance of several machine learning (ML) anomaly detectors in detecting them. We test a diverse set of six anomaly detectors, each based on a different ML algorithm, on nine scenarios containing leaks and anomalies of various kinds. The proposed approach is very effective at quickly (within hours) identifying the presence of a leak, with a limited number of false positives. HIGHLIGHTS We focus on the detection of leaks and anomalies occurring in the district metered areas (DMAs).; We use machine learning anomaly detection algorithms on hourly inflow, loss, consumption and pressure data.; We test the proposed approach on nine scenarios and show its good performance, potentially finding leaks within hours, with a limited number of false positives.

Interactive kinesiotherapy for children with flat foot

W pracy przedstawiono nowatorską metodę interaktywnej rehabilitacji dzieci z wadami narządu ruchu. Może ona być zastosowana do ćwiczeń profilaktyczno-korekcyjnych szczególnie dzieci z płaskostopiem. Rehabilitacja przy pomocy tego typu kinezyterapii odbywa się na zasadzie nieświadomej zabawy podczas gry na komputerze.The innovative method of interactive kinesiotherapy for children with fiat foot is presented in this paper. The rehabilitation will be carry out with use of special device as an unaware fun during a game on the computer