12th EASN International Conference on "Innovation in Aviation & Space for opening New Horizons"

Epoxy resins show a combination of thermal stability, good mechanical performance, and durability, which make these materials suitable for many applications in the Aerospace industry. Different types of curing agents can be utilized for curing epoxy systems. The use of aliphatic amines as curing agent is preferable over the toxic aromatic ones, though their incorporation increases the flammability of the resin. Recently, we have developed different hybrid strategies, where the sol-gel technique has been exploited in combination with two DOPO-based flame retardants and other synergists or the use of humic acid and ammonium polyphosphate to achieve non-dripping V-0 classification in UL 94 vertical flame spread tests, with low phosphorous loadings (e.g., 1-2 wt%). These strategies improved the flame retardancy of the epoxy matrix, without any detrimental impact on the mechanical and thermal properties of the composites. Finally, the formation of a hybrid silica-epoxy network accounted for the establishment of tailored interphases, due to a better dispersion of more polar additives in the hydrophobic resin

Increasing the Reliability of Power and Communication Networks via Robust Optimization

Uncertainty plays an increasingly significant role in the planning and operation of complex networked infrastructure. The inclusion of variable renewable energy in power systems makes ensuring basic grid requirements such as transmission line constraints and the power balance between supply and demand more involved. Likewise, data traffic in communication networks varies greatly with user preferences and service availability, and with communication networks carrying more traffic than ever due to the surge in network-enabled devices, coping with the highly variable data flows between server and end-users becomes more crucial for the network's overall stability. Within this context, we propose in this thesis new adaptable methods for optimizing flows in power and communication systems that explicitly consider the growing variability in these systems to guarantee optimal operation with a flexible degree of reliability. The proposed methods use a robust optimization framework, making constraints dependent on uncertain factors tractable by replacing originally stochastic conditions with deterministic counterparts. The primary benefit of robust methods is that they ensure the system is feasible for any values of the uncertain variables within a given continuous set of possible realizations. This, however, can lead to excessively conservative solutions. Therefore, we also investigate how to reduce the conservativeness of the proposed algorithms. This thesis focuses on two classes of problems in power and communication systems, flow control and the placement of flow-controlling devices. In power systems, flow control refers to actions that induce changes in the power carried by transmission lines to minimize or maximize a specific objective value while considering the electrical grid's physical constraints. Some examples of power flow control actions are the change of switching equipment's state, regulation of generators' set points, and the management of the so-called Flexible AC Transmission Systems (FACTS) devices. For the last two action types, we propose a robust approach to optimize the corresponding control policies. As for communication networks, (data) flow control is implemented at each router in the network. These routers define the path and the rate data is forwarded using routing tables. We show that it is possible to robustly design policies to adapt these routing tables that optimize the data flows in the network depending on the instantaneous rate of the system's exogenous inputs. For both flow problems, we employ a robust optimization framework where affine-linear functions parametrize the flow control policies. The parametrized policies can be efficiently computed via linear or quadratic programming, depending on the system's constraints. Furthermore, we consider the placement problems in the form of FACTS placement and the embedding of virtual networks in an existing communication network to improve the reliability of the network systems. Both problems are formulated as robust Mixed-Integer Linear Programs (MILP). However, because finding provable optimal solutions in large networks is computationally challenging, we also develop approximate algorithms that can yield near-optimal results while being several times faster to solve than the original MILP. In the proposed robust framework, the flow control and the placement of controlling-devices problems are solved together to take into account the coupling effects of the two optimization measures. We demonstrate the proposed methodology in a series of use cases in power and communication systems. We also consider applications in Smart Grids, where communication and electric networks are closely interlinked. E.g., communication infrastructure enables real-time monitoring of the status of power grids and sending timely control signals to devices controlling the electric flow. Due to the increasing number of renewable energy resources, Smart Grids must adapt to fast changes in operating conditions while meeting application-dependent reliability requirements. The robust optimization methods introduced in this thesis can thus use the synergy between flexible power and communication systems to provide secure and efficient Smart Grid operation

Low power digital baseband core for wireless Micro-Neural-Interface using CMOS sub/near-threshold circuit

This thesis presents the work on designing and implementing a low power digital baseband core with custom-tailored protocol for wirelessly powered Micro-Neural-Interface (MNI) System-on-Chip (SoC) to be implanted within the skull to record cortical neural activities. The core, on the tag end of distributed sensors, is designed to control the operation of individual MNI and communicate and control MNI devices implanted across the brain using received downlink commands from external base station and store/dump targeted neural data uplink in an energy efficient manner. The application specific protocol defines three modes (Time Stamp Mode, Streaming Mode and Snippet Mode) to extract neural signals with on-chip signal conditioning and discrimination. In Time Stamp Mode, Streaming Mode and Snippet Mode, the core executes basic on-chip spike discrimination and compression, real-time monitoring and segment capturing of neural signals so single spike timing as well as inter-spike timing can be retrieved with high temporal and spatial resolution. To implement the core control logic using sub/near-threshold logic, a novel digital design methodology is proposed which considers INWE (Inverse-Narrow-Width-Effect), RSCE (Reverse-Short-Channel-Effect) and variation comprehensively to size the transistor width and length accordingly to achieve close-to-optimum digital circuits. Ultra-low-power cell library containing 67 cells including physical cells and decoupling capacitor cells using the optimum fingers is designed, laid-out, characterized, and abstracted. A robust on-chip sense-amp-less SRAM memory (8X32 size) for storing neural data is implemented using 8T topology and LVT fingers. The design is validated with silicon tapeout and measurement shows the digital baseband core works at 400mV and 1.28 MHz system clock with an average power consumption of 2.2 μW, resulting in highest reported communication power efficiency of 290Kbps/μW to date

Computational Intelligence Application in Electrical Engineering

The Special Issue "Computational Intelligence Application in Electrical Engineering" deals with the application of computational intelligence techniques in various areas of electrical engineering. The topics of computational intelligence applications in smart power grid optimization, power distribution system protection, and electrical machine design and control optimization are presented in the Special Issue. The co-simulation approach to metaheuristic optimization methods and simulation tools for a power system analysis are also presented. The main computational intelligence techniques, evolutionary optimization, fuzzy inference system, and an artificial neural network are used in the research presented in the Special Issue. The articles published in this issue present the recent trends in computational intelligence applications in the areas of electrical engineering