Tutorial Presentations

  • Detection and Forecasting of Micro-Scale Variability in Electric Power Profiles

    Abstract: The development of advanced metering infrastructures (AMI) for low-voltage (LV) networks enables the fine-grained collection of rich and diverse datasets for in situ power quality assessment. Resulting datasets, collected at high reporting rates, support detection and labelling of micro-scale events that are affecting the correct operation of LV networks and have been so far overseen through window-based averaging using typical approaches and measurement equipment. The tutorial focuses on methods and techniques to first detect and label such events as anomalies in a data processing and learning pipeline. Subsequently, the labeled datasets are used in a forecasting framework as early-warning system for potential imbalances in the local energy network. One key novelty is the combination of extracted features using time series data mining methods, such as the matrix profile, with state-of-the-art machine learning algorithms, including automated machine learning to optimize classification metrics in real time, across various model/algorithm structures and hyper-parametrisation options. Hands-on activities will highlight the practical use of the Python matrixprofile, scikit-learn and auto-sklearn open-source packages on publicly available residential power measurements collected in the context of two active research projects.

  • Material State Determination For Process State Awareness 2023

    Abstract: Materials State Awareness (MSA) research and development has advanced science and technology that addresses the sensing of the material’s state at the microstructure level. A crucial element of MSA is predicting the future state of the material system. MSA technology can be used to predict and control process states. The key difference between Process State Awareness (PSA) and standard control techniques is that the in-situ materials characterization of the product is a major input into the control system along with standard industrial process sensors. Successful implementation of a PSA project will require close collaboration between sensor researchers, materials experts and measurement specialists.

    This tutorial will discuss PSA development efforts within industrial, national lab, and medical sectors with an emphasis on continuous processes. The MSA techniques and philosophy will be confirmed to be at the core of PSA development that will determine and predict the state of a process. 

  • Non-Contact In-Circuit Impedance Measurement Technology

    Abstract: In-circuit impedance measurement extracts impedance information of an energized electrical system. The extracted impedance serves as a key parameter in many practical industrial applications, such as optimal electromagnetic interference (EMI) filter design, condition monitoring of critical electrical assets, and state-of-charge and state-of-health of battery, etc. Therefore, the research and development of a simple, safe, and accurate in-circuit impedance measurement methodology serves to fulfil this role. This tutorial presents the past and existing research work from the instructors. They have developed a series of novel in-circuit impedance measurement setups that do not require direct electrical contact with the energized electrical system under test, and thus simplifying on-site implementation and eliminating potential electrical safety hazards. Firstly, the significance of in-circuit impedance will be introduced, followed an overview of common measurement setups, and then the proposed in-circuit impedance measurement setups will be elaborated. The application aspects of the proposed technology will be presented, such as EMI filter design in power converters, stator winding fault detection of induction motors, power rail condition monitoring of mass rapid transit (MRT), current collector health monitoring of light rail transit (LRT), and voltage-dependent capacitances extraction of power semiconductors, etc.

  • Revisiting Sensor Characterization and Measurement Chain Optimization under Information Theory

    Abstract: Sensor characterization and metrology have always been complex issues due to the different design approaches and application tasks. Therefore, concepts such as resolution, precision, accuracy, and minimum detectable signal are sometimes characterized by misleading and fuzzy definitions. In the first part of this tutorial, I will review the basic concept of resolution under the Information Theory framework showing its strong relationship with mutual information. Then, I will analyze a signal measurement chain as a communication channel where, differently from the telecommunication approach, the optimization is performed on the channel configuration instead of the coding process.  

    Additionally, to the above viewpoint, I will show how electronic measurement acquisition design could be reduced to three constraints: information, time, and power consumption, and how they could be used to enhance the overall performance in terms of resolution, bandwidth, and power efficiency reducing the design time. Finally, I will illustrate how the single gains in a measurement chain could be optimized concerning the above constraints by examples. 

    Part1: Concept of information in measurement and sensing. This part will frame some concepts of information theory under the sensor design viewpoint showing the difference compared to the typical telecommunication applications. The concept of resolution will be revisited under the mutual information paradigm.  

    Part 2: The three-node constraints scheme. This section will show how any sensing design could be simplified using the trade-offs between information, time, and power. The first is the information node, where resolution, operating range, and input-referred noise are put together to define the information conveyed by the system. Then, the power consumption node will be treated, showing the most common sensor figure of merit (FoM) factors. Finally, the time node will address the bandwidth relationships.  

    Part 3: Optimization of measurement and sensors acquisition chains. One of the most challenging problems in most sensor acquisition chains is understanding how to balance the gain of stages to optimize the overall system. Therefore, a complete system of equations is set for this task, including the resolution rules of acquisition chains (RRC). Finally, examples of design optimization will be shown. 


    Abstract: There has been an astronomical increase in the number of technical paper submissions in the past decade.  Some of the reasons include: 

    • pressure to publish, as the success indicator, for promotion and professional advancement,
    • universities moving away from the traditional M.S. Theses and Ph.D. Dissertations to instead a compilation of several peer-reviewed journal papers,
    • creation of new journals, and
    • the open-access publishing “economy”.

    Journals are ranked according to certain “indicators” that may or may not be objective. Everyone wants to publish in the highest-ranking journals exasperating the situation for some. However, we wish to think that “Quality” is the number one “indicator” of a journal. “Quality” is not a “measurable” and is difficult to define. However, there are ways by which to positively influence the “Quality” of a journal beyond those indicators.

    REVIEW PROCESS – This is the most crucial aspect of the publishing process.  It involves the authors, the editors, and the reviewers, and how each performs detrimentally impacts the outcome and the “Quality”. Without proper training, knowledge, experience and established guidelines, this entire process is destined to achieve mediocrity or fail all together. 

    This educational tutorial aims to provide basic guidelines and critical tips for everyone involved in this process.

  • Microwave Instrumentation and Measurement for Nanotechnology Materials and Devices

    Abstract: Non-destructive Testing (NDT) NDTE is the process of inspecting, testing and evaluating materials, components or systems without altering their properties. Leading industries use NDT techniques to ensure the quality of products. Among established NDT techniques (visual testing, eddy-current, magnetic-particle, liquid penetrant, radiographic and ultrasonic), microwave (300MHz-30GHz) and mm-Wave (30GHz-300GHz) electromagnetic waves present advantages such as penetration inside dielectric materials, low power, high electrical sensitivity to both physical and geometric properties, non-contact and non-ionizing characterization. Despite advantages of Microwave NDT techniques over established methods, their penetration at industrial scale is still limited and confined to niche markets or academic laboratories. The inadequate commercial availability of microwave systems for NDT purposes has limited its more extensive implementation. Indeed, quantitative evaluation of materials requires dedicated calibration algorithms, protocols, standard reference materials, and essential training in GHz technologies not available at the industry level. Introduction of nanotechnology has a great potential to enhance material properties but deeper understanding of local interactions between nanoparticles and host materials, interfaces effects, correlation between nano- and macroscale responses, require innovative industrial characterization methods and tools to enhance final materials or/and products, and to screen new material properties. State of the art multi-scale microwave & mm-Wave instrumentation will be discussed with probing of electromagnetic properties from nano- to macroscale. Rather than a usual and restricted laboratory equipment approach, the scientific investigation, hardware development and applicability to industrial challenges (including development of multi-scale references and standards, fully automated and real-time operations) are thought as a whole.

    Syllabus: 1) Fundamentals of Wave to Material interaction, 2) RF and Microwave instrumentation for NDT applications, 3) From Radar to Nano-Radar concept, 4) Metrology – Traceability aspects

  • Predictive Maintenance with Digital Twin

    Abstract: The industry is migrating from reactive to predictive maintenance to increase operational availability and efficiency. An exciting chance to facilitate this transformation is coming with the 4th industrial revolution enabled by new information and communication technology (ICT) and data-intensive methodologies. The digital twin is a disruptive technology that creates a living model of industrial assets. The digital twin living model will continually adapt to changes in the environment or operations using real-time sensory data and forecast the future of the physical target. A digital twin can be used to proactively identify potential issues with its real physical counterpart. It allows the prediction of the remaining useful life of the physical twin by leveraging a combination of physics-based models and data-driven analytics. The digital twin ecosystem comprises sensor and measurement technologies, industrial Internet of Things, simulation and modeling, machine learning, artificial intelligence, and data/information fusion. This tutorial will give an overview of the opportunity offered by the digital twin technology for predictive maintenance and identify the potential challenges for digital twin research and development from the industrial asset life cycle management perspective.