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Analysis of the stator current prediction capabilities in induction motor drive using the LSTM network

100%

Teler K. , Orlowska-Kowalska T.

tom Vol. 8 (43)

31-52

In modern areas of knowledge related to electric drive automation, there is often a need to predict the state variables of the drive system state variables, such as phase current and voltage, electromagnetic torque, stator and rotor flux, and others. This need arises mainly from the use of predictive control algorithms but also from the need to monitor the state of the drive to diagnose possible faults that have not yet occurred but may occur in the future. This paper presents a method for predicting stator phase current signals using a network composed of long-short-term memory units, allowing the simultaneous prediction of two signals. The developed network was trained on a set of current signals generated by software. Its operation was verified by simulation tests in a direct rotor flux-oriented control (DRFOC) structure for an induction motor drive in the Matlab/Simulink environment. An important property of this method is the possibility of obtaining a filtering action on the output of the network, whose intensity can be controlled by varying the sampling frequency of the training signals.

Wykrywanie anomalii w zachowaniu robota usługowego z wykorzystaniem sieci LSTM

72%

Lis K. , Szynkiewicz W. , Niewiadomska-Szynkiewicz E. , Ciecierski K.

tom z. 197, t. 1

211-222

Praca dotyczy zagadnień cyberbezpieczeństwa mobilnych robotów usługowych z rozproszoną architekturą sterowania. Prezentowane jest autorskie podejście do detekcji anomalii w działaniu robota na podstawie odczytów z czujników, przy założeniu, że ataki skutkują niezgodnym z zakładanym zachowaniem robota. Opracowany system wykrywania włamań RIDS wykorzystuje głębokie, rekurencyjne sieci neuronowe. W pracy przedstawiona jest architektura sieci, jej parametry oraz atrybuty, na podstawie których identyfikowane są potencjalne ataki. Rozwiązanie zostało zweryfikowane w środowisku laboratoryjnym z wykorzystaniem robota TIAGo firmy PAL Robotics. Wyniki badań potwierdzają, że proponowany system może skutecznie wspierać proces wykrywania zagrożeń komputerowych wpływających negatywnie na funkcjonowanie systemów robotycznych.

The paper addresses cybersecurity issues of mobile service robots with distributed control architecture. A novel robot intrusion detection system (RIDS) that employs deep recurrent neural networks to detect anomalies in robot performance based on sensor readings, under the assumption that attacks result in incompatible robot behavior is described. The performance of the RIDS system was evaluated in a laboratory environment using the TIAGo robot from PAL Robotics. The presented experimental results confirm that RIDS can effectively support the process of detecting computer threats that negatively affect the service robot operating.

Shallot Price Forecasting Models: Comparison among Various Techniques

72%

Kasemset C. , Phuruan K. , Opassuwan T.

2023

tom Vol. 29, Iss. 4

348-355

Shallot is one of several horticultural products exported from Thailand to various countries. Despite an increase in shallot prices over the years, farmers face challenges in price forecasting due to fluctuations and other relevant factors. While different forecasting techniques exist in the literature, there is no universal approach due to varying problems and datasets. This study focuses on predicting shallot prices in Northern Thailand from January 2014 to December 2020. Traditional and machine learning models, including ARIMA, Holt-Winters, LSTM, and ARIMA-LSTM hybrids, are proposed. The LSTM model considers temperature and rainfall as influencing factors. Evaluation metrics include RMSE, MAE, and MAPE. Results indicate that the ARIMA-LSTM hybrid model performs best, with RMSE, MAE, and MAPE values of 10.275 Baht, 8.512 Baht, and 13.618%, respectively. Implementing this hybrid model can provide shallot farmers with advanced price information for informed decision-making regarding cultivation expansion and production management.