Proceedings 2020 IEEE East-West Design & Test Symposium (EWDTS)
The main target of the IEEE East-West Design & Test Symposium (EWDTS) is to exchange experiences between scientists and technologies from Eastern and Western Europe, as well as North America and other parts of the world, in the field of design, design automation and test of electronic circuits and systems. The symposium is typically held in countries around East Europe, the Black Sea, the Balkans and Central Asia region. We cordially invite you to participate and submit your contributions to EWDTS 2020 which covers (but is not limited to) the following topics. • Analog, Mixed-Signal and RF Test • ATPG and High-Level TPG • Automotive Reliability & Test • Built-In Self Test • Debug and Diagnosis • Defect/Fault Tolerance and Reliability • Design Verification and Validation • EDA Tools for Design and Test • Embedded Software • Failure Analysis & Fault Modeling • Functional Safely • High-level Synthesis • High-Performance Networks and Systems on a Chip • Internet of Things Design & Test • Low-power Design • Memory and Processor Test • Modeling & Fault Simulation • Network-on-Chip Design & Test • Flexible and Printed Electronics • Applied Electronics Automotive/Mechatronics • Algorithms • Object-Oriented System Specification and Design • On-Line Testing • Power Issues in Design & Test • Real Time Embedded Systems • Reliability of Digital Systems • Scan-Based Techniques • Self-Repair and Reconfigurable Architectures • Signal and Information Processing in Radio and Communication Engineering • System Level Modeling, Simulation & Test Generation • System-in-Package and 3D Design & Test • Using UML for Embedded System Specification • Optical signals in communication and Information Processing • CAD and EDA Tools, Methods and Algorithms • Hardware Security and Design for Security • Logic, Schematic and System Synthesis • Place and Route • Thermal and Electrostatic Analysis of SoCs • Wireless and RFID Systems Synthesis • Sensors and Transducers • Medical Electronics • Design of Integrated Passive Components
This article describes the issues of analysis and assessment of the human factor for predicting the violation committed by the locomotive driver when driving the electric rolling stock. An intelligent system overview for assessing the likelihood of a violation by a locomotive driver is given. Such a system can generate recommendations depending on previously committed violations. One of the tasks is to reduce the risk of locomotive safety devices malfunctions, which are part of the locomotive electrical equipment. The solution to the problem of predicting the occurrence of possible violations is solved using tools and machine learning algorithms. A model has been built that generates recommendations for the driver based on information about previously committed violations and several static characteristics of the locomotive driver.