Welcome to my research homepage!
Sensors; Large scale and distributed sensor systems; Sensor information processing; Statistical learning and computational intelligence techniques for sensor data; New paradigms: (Industrial-) Internet of Things, Cyber-physical Systems; Sensors in Automation and Control; Applications: industry, energy systems, smart buildings and smart cities.
- [02/21] Elected secretary of the IEEE Romania Section
- [01/21] Started two new research projects:
- HALYomorpha halys IDentification: Innovative ICT tools for targeted monitoring and sustainable management of the brown marmorated stink bug and other pests (HALY.ID), ERA-NET CT-AGRI-FOOD, 2021-2023 Project website
- Extending the Measurement Concept for the Control of Emerging Power Systems (EMERGE), UEFISCDI Exploratory Research Projects Call 2020, 2021-2023 Project website
- [12/20] Invited session on Intelligent data processing from sensors in control and decision support systems - MED 2021
- [11/20] Started the ERASMUS+ Project “Master of Engineering in Internet of Things” (IoTrain) 2020-2023 Project website
- [10/20] Special Issue on “Energy Management for Smart Buildings” - Energies
- [09/20] Appointed Associate Editor for IEEE Access
- [08/20] Launched the 2020 IEEE Robotics and Automation Romania Chapter Best PhD Thesis Award Competition
- [07/20] Our paper on “Model for Optimized EVSE Deployment in Dense Urban Areas” has been accepted for publication at the 46th Annual Conference of the IEEE Industrial Electronics Society (IES) IECON 2020
- [06/20] Appointed Co-Editor-in-Chief of the International Journal of Computing
- [05/20] Our paper on “Learning Dominant Usage from Anomaly Patterns in Building Energy Traces” has been accepted for publication at the 2020 IEEE 16th International Conference on Automation Science and Engineering CASE 2020
- [12/19] Obtained the Habilitation (Dr.-Ing. habil.) certificate for advising PhD students in Automatic Control and Industrial Informatics – Systems Engineering domain.
- [09/19] Special Issue on “Convergence of Intelligent Data Acquisition and Advanced Computing Systems” - Sensors
- 2021 TPCs: IEEE SECON, CPS-IoTBench, CESCIT, ECAI, IDAACS, IEEE MASS
- 2020 TPCs: IWSSS, INTAP, FEDCSIS, EAIS, IDAACS-SWS, DESSERT
- 2019 TPCs: IDAACS, IWSSS, INTAP, ICPADS, AICT, EAIS
- Editor: Journal of Sensors, International Journal of Computing, Smart Cities, Sensors (MDPI), IEEE Access
- Reviewer: Applied Energy, IEEE Sensors, Engineering Applications of Artificial Intelligence
- Senior member of IEEE and Robotics and Automation Society chapter chair; Member of IEEE RAS Technical Committee on Smart Buildings
PhD Research Topics
- Open information systems for energy management in large commercial buildings
Large commercial buildings offer significant economic and environmental incentives for improved energy management under growing urbanization tendencies in smart cities. Current monitoring and automation systems are mostly closed hardware-software solutions with high associated costs. The objective is to develop a new methodology for integration of open source components in the automation of modern buildings. This will bridge wired and wireless communication protocols such as Modbus, BACNet, ZigBee, hardware interfaces and software libraries, structured data representations and learning algorithms for modelling and control.
- Efficient methods for multivariate time series processing for forecasting and anomaly detection
Many industrial processes are monitored through tens to thousands of continuous and discrete sensors producing rich data traces at various timescales. The objective is to first perform a critical evaluation among conventional time series modelling algorithms e.g. multivariate SARIMA, against new machine learning and deep learning models e.g. recurrent and convolutional neural networks, for forecasting and anomaly detection tasks in typical industrial scenarios. Second, the viability of online inference for these types of models will be investigated through targeted case studies e.g. multilevel direct and indirect energy measurement in smart buildings and production facilities.
- Large scale monitoring by means of distributed sensor networks allocated
Distributed sensor networks involve large numbers of sensing, computing and communication nodes that collaborate for joint observation of interest areas. Embedded consensus algorithms allow improved operation through data reduction and better quality of information. The objective is to develop new methods for distributed sensing in large scale applications that increase network lifetime, resilience and robustness for critical tasks. Implementation and benchmarking will be handled both in simulation and on a dedicated test bed infrastructure in connection to predefined KPIs.
- Information extraction and in situ control for distributed systems of sensors and actuators
As the on-board resources of embedded sensing and actuation nodes increase, local extraction of relevant information becomes possible as well as embedded predictive control schemes that use this information. This leads to a flatter automation hierarchy composed of cooperating intelligent periphery and an on-demand cloud layer providing advanced control/optimization as a service. The objective is to develop and validate such approaches in both simulation (MATLAB/Simulink) and small scale pilot deployments (two/three tank system, flexible assembly line).
- Convergence of complex information systems and automation systems within new paradigms such as IIoT and CPS allocated
Modern automation systems increasingly leverage advanced information technologies and protocols for data access and exchange, security, visualisation and reporting. These in turn are being adapted to specific application domains in the industry using significant domain expertise. The objective is to identifiy first the key areas of overlap and differentiation and proposed a targeted approach that models this convergence, also known as IT/OT integration. A demonstrator platform will be built based on COTS technologies and components such as industrial development boards for monitoring and control, in order to validate the feasability of the proposed solutions.
- Reinforcement learning for energy management