Functional Pattern-Related Anomaly Detection Approach Collaborating Binary Segmentation with Finite State Machine

The process control-oriented threat,which can exploit OT(Operational Technology)vulnerabilities to forcibly insert abnormal control commands or status information,has become one of the most devastating cyber attacks in industrial automation control.To effectively detect this threat,this paper proposes one functional pattern-related anomaly detection approach,which skillfully collaborates the BinSeg(Binary Segmentation)algorithm with FSM(Finite State Machine)to identify anomalies between measuring data and control data.By detecting the change points of measuring data,the BinSeg algorithm is introduced to generate some initial sequence segments,which can be further classified and merged into different functional patterns due to their backward difference means and lengths.After analyzing the pattern association according to the Bayesian network,one functional state transition model based on FSM,which accurately describes the whole control and monitoring process,is constructed as one feasible detection engine.Finally,we use the typical SWaT(Secure Water Treatment)dataset to evaluate the proposed approach,and the experimental results show that:for one thing,compared with other change-point detection approaches,the BinSeg algorithm can be more suitable for the optimal sequence segmentation of measuring data due to its highest detection accuracy and least consuming time;for another,the proposed approach exhibits relatively excellent detection ability,because the average detection precision,recall rate and F1-score to identify 10 different attacks can reach 0.872,0.982 and 0.896,respectively.

Power Factor Correction Rectifier with a Variable Frequency Voltage Source in Vehicular Application

This paper presents a PFCVF (Power Factor Correction) rectifier that uses a variable frequency source for alternators for electric and hybrid vehicles application. In such application, the frequency of the signal in the alternator changes according to the vehicle speed, more over the loading effect on the alternator introduces harmonic currents and increases the alternator apparent power requirements. To overcome these problems and aiming more stability and better design of the alternator, a new third harmonic injection technique is proposed. This technique allows to preserve a good THD (Total Harmonic Distortion) of the input source at any frequency and to decrease losses in semiconductors switches, thereby allowing more stability and reducing the apparent power requirements. A comparative study between the standard and the new technique is made and highlights the effectiveness of the new design. A detailed analysis of the proposed topology is presented and simulations as well as experimental results are shown.

Data-driven prediction of temperature variations in an open cathode proton exchange membrane fuel cell stack using Koopman operator

In this study,a novel application of the Koopman operator for control-oriented modeling of proton exchange membrane fuel cell(PEMFC)stacks is proposed.The primary contributions of this paper are:(1)the design of Koopman-based models for a fuel cell stack,incorporating K-fold cross-validation,varying lifted dimensions,radial basis functions(RBFs),and prediction horizons;and(2)comparison of the performance of Koopman-based approach with a more traditional physics-based model.The results demonstrate the high accuracy of the Koopman-based model in predicting fuel cell stack behavior,with an error of less than 3%.The proposed approach offers several advantages,including enhanced computational efficiency,reduced computational burden,and improved interpretability.This study demonstrates the suitability of the Koopman operator for the modeling and control of PEMFCs and provides valuable insights into a novel control-oriented modeling approach that enables accurate and efficient predictions for fuel cell stacks.