Dieses Bild zeigt Yuliang Ma

Yuliang Ma


Akademischer Mitarbeiter
Institut für Automatisierungstechnik und Softwaresysteme


+49 711 685 69198
+49 711 685 67302

Pfaffenwaldring 47
70550 Stuttgart
Raum: 3.252

Zeitschriften und Konferenzen:
  1. 2023

    1. P. Grimmeisen, R. Golwalkar, Y. Ma, und A. Morozov, „Automated and Continuous Risk Assessment for ROS-Based Software-Defined Robotic Systems“, 2023, S. 1–7.
    2. Y. Ma, P. Grimmeisen, und A. Morozov, „Case Study: ROS-Based Fault Injection for Risk Analysis of Robotic Manipulator“, in 2023 IEEE 19th International Conference on Automation Science and Engineering (CASE), 2023, S. 1--6.
  2. 2022

    1. P. Grimmeisen, M. Diaconeasa, Y. Ma, und A. Morozov, „Automated Generation of Hybrid Probabilistic Risk Models from SysML v2 Models of Software-Defined Manufacturing Systems“, in Morozov ASME 2022 International Mechanical Engineering Congress & Exposition (IMECE 2022), 30.Oct-3. Nov. 2022, 2022, 2022.
    2. Y. Ma, P. Grimmeisen, und A. Morozov, „Detection and classification of robotic manipulator anomalies using MLSTM-FCN models“, ASME 2022 International Mechanical Engineering Congress & Exposition (IMECE 2022), 30.Oct-3. Nov. 2022, 2022, 2022.
  3. 2021

    1. Y. Ma, A. Morozov, und S. Ding, „Anomaly Detection for Cyber Physical Systems using Transformers“, The International Mechanical Engineering Congress and Exposition, IMECE, 2021.

Forschungsschwerpunkt: - Intelligent Anomaly Interpreter for Cyber-Physical Systems

Beschreibung: - Deep Learning-based Anomaly Detection (DLAD) methods for Cyber-Physical-Systems have shown high efficiency and accuracy over traditional anomaly detection techniques. However, the lack of interpretability has impeded
practical applications of DLAD methods. Especially for safety-critical scenarios such as Human-Robot Cooperation and Assistive Medical Robotics, it is necessary to ensure that these systems are inspected robustly and carefully. The intelligent anomaly interpreter for DLAD methods needs to solve the following problems:

• Establish trust between models and users: It is not sufficient that current DLAD models provide binary results (normal or abnormal) to safety inspectors. The reason behind a specific anomaly is also supposed to be reported to users, which can help users take further actions for the whole system. For example, feature-based anomaly interpreters can explain an anomaly via finding the most abnormal data behavior in monitored multivariate timeseries data, which can offer the reason why this point is an anomaly.

• Enable fault diagnosis for CPS: Without sufficient understanding of the decision made by DLAD models, it is almost not possible to diagnose system when an anomaly occurs. For example, for the same abnormal data behavior of multivariate time-series data generated from CPS, the root cause can be different. And vice versa, different failure modes could happen after the same fault injection action. To do fault diagnosis, expert knowledge is needed for a specific CPS.

• Reduce false positives: High rate of false positives (FP) poses a critical challenge for deploying DLAD models into practical safety-critical CPS applications. Anomaly interpreter needs to give evidence to explain whether an outlier will eventually be an anomaly or not. For example, not every wrong sensor data reported to the controller will cause a failure. Thus, the context of an anomaly also needs to be considered when we deploy DLAD methods to CPS applications.


Google Scholar: https://scholar.google.com/citations?user=vfJJEZAAAAAJ&hl=zh-CN

ResearchGate: https://www.researchgate.net/profile/Yuliang-Ma-4





Akademische Mitarbeiter

Digitaler Zwilling für die Automatisierungstechnik

Intelligente und lernende Automatisierungssysteme

Komplexitätsbeherrschung in der Automatisierungstechnik

Risikoanalyse und Anomalieerkennung für vernetzte Automatisierungssysteme

Stipendiat Graduate School of Excellence advanced Manufacturing Engineering (GSaME)


Zum Seitenanfang