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The models of multi-zone electric ship is important to the development of ship operational capability and performance. However, there is not one best model type that can fit all the needs of the engineering process. High-fidelity models are needed to act as a digital twin to the system hardware for testing and validation purposes. However, a highly detailed digital model of a MVDC does not enable insight and development of analytical control and optimization algorithms. This paper presents a reduced order model (ROM) of a notional four-zone medium voltage ship. This ROM can be written in a closed-form analytical expression that is appropriate for analysis and high-level supervisory control synthesis.

In this paper, the effects and mitigation strategies of pulsed loads on medium voltage DC (MVDC) electric ships are explored. Particularly, the effect of high-powered pulsed loads on generator frequency stability are examined. As a method to stabilize a generator which has been made unstable by high-powered pulsed loads, it is proposed to temporarily extract energy from the propulsion system using regenerative propeller braking. The damping effects on generator speed oscillation of this method of control are examined. The impacts on propeller and ship speed are also presented.

The growth of solar photovoltaic (PV) systems in distribution networks has brought about new challenges to the operation of protection devices. The new and irregular bidirectional flow patterns from PV systems may complicate the coordination of protection relays designed to detect faults. This paper highlights some of the more concerning operational scenarios of PV systems at the farm level on typical time-current overcurrent grading of Type 51 relays. A number of simulation case studies are carried out for a small radial-type distribution system considering different PV penetration or irradiance levels, fault locations and PV sites. The impacts on overall protection coordination are discussed.

ESA Space Weather Service Network supports end users in a wide range of affected sectors, in mitigating the effects of space weather on their systems, reducing costs and improving reliability. In this network, space weather products and tools are developed and federated in services, that are suitable for operational implementation to meet the end user needs. In the aim to establish a close relationship with the end users of space weather services, the SSA Space Weather Coordination Centre (SSCC) organizes user support campaigns to build tailored space weather bulletins. During the campaign, the SSCC works together with the user in order to compile dedicated space weather forecast notifications. We highlight here the SSCC user support campaign for a group of test users within the GNSS community.

Although research has shown the benefits of solar photovoltaic (PV) systems penetrating onto distribution grids, improperly sized and placed PVs can cause extreme over-voltages, higher power losses and grid capacity violations. In some cases, high PV penetration may lead to reverse power flow conditions and disruption of existing protection schemes. This work proposes improvements to an existing multi-objective optimization genetic algorithm (IMO) technique designed for PV sizing and site locations in radial distribution systems based on power loss reduction and limiting nodal voltage deviation. The proposed mathematical model yields a solution set of PV sizes based on bus location which reduces the instances of voltage profile constraint violations across all branches of the system while improving power losses at each nodal location. This is an improvement over previous works which were only applicable to single radial feeders without any branches. Finally, this approach allows for optimization of multiple PV distributed generation to be used to compensate losses and improve the overall voltage profile on any branch of the radial system, with room for future expansion.

Hand function rehabilitation is one of the most important aspects to improve the quality of life and independence for stroke survivors. Access to therapy is a key factor in one’s ability to perform rehabilitation exercises and recover motor function. The aim of this research is to show the viability of using a soft robotic glove to perform home-based rehabilitation of the hand function of stroke survivors. Soft wearable robotic devices are a promising approach to hand rehabilitation due to their lightweight, compliant, and low-cost design. The goal of this wearable technology is to reduce hyperexcitability of the flexor muscles through cyclic stretching of the fingers, active-assisted exercise, and task-oriented training. In this pilot study, four chronic stroke survivors were provided 300 repetitions of opening and closing their hand with the soft robotic glove over 6 training sessions. This high-intensity, high-repetition protocol was found to be safe and well-tolerated by all subjects. Results demonstrated average increases of 18.5° and 17.3° in the relaxed metacarpophalangeal and proximal interphalangeal joint angles, respectively, of the index finger and an average increase of 33 Newtons in grip strength. This work motivates further development of this home-based soft robotic rehabilitation device and lays the groundwork for a longer term study.

This paper investigates trajectory prediction for robotics, to improve the interaction of robots with moving targets, such as catching a bouncing ball. Unexpected, highly-non-linear trajectories cannot easily be predicted with regression-based fitting procedures, therefore we apply state of the art machine learning, specifically based on Long-Short Term Memory (LSTM) architectures. In addition, fast moving targets are better sensed using event cameras, which produce an asynchronous output triggered by spatial change, rather than at fixed temporal intervals as with traditional cameras. We investigate how LSTM models can be adapted for event camera data, and in particular look at the benefit of using asynchronously sampled data., 5 pages, 5 figures, 1 table, paper accepted for presentation at the 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS2020)

Fast, non-linear trajectories have been shown to be more accurately visually measured, and hence predicted, when sampled spatially (that is when the target position changes) rather than temporally, i.e. at a fixed-rate as in traditional frame-based cameras. Event-cameras, with their asynchronous, low latency information stream, allow for spatial sampling with very high temporal resolution, improving the quality of the data and the accuracy of post-processing operations. This paper investigates the use of Long Short-Term Memory (LSTM) networks with event-cameras spatial sampling for trajectory prediction. We show the benefit of using an Encoder-Decoder architecture over parameterised models for regression on event-based human-to-robot handover trajectories. In particular, we exploit the temporal information associated to the events stream to predict not only the incoming spatial trajectory points, but also when these will occur in time. After having studied the proper LSTM input/output sequence length, the network performance are compared to other regression models. Then, prediction behavior and computational time are analysed for the proposed method. We carry out the experiment using an iCub robot equipped with event-cameras, addressing the problem from the robot perspective.

Soft strain sensors have been explored as an unobtrusive approach for wearable motion tracking. However, accurate tracking of multi degree-of-freedom (DOF) noncyclic joint movements remains a challenge. This paper presents a soft sensing shirt for tracking shoulder kinematics of both cyclic and random arm movements in 3 DOFs: adduction/abduction, horizontal flexion/extension, and internal/external rotation. The sensing shirt consists of 8 textile-based capacitive strain sensors sewn around the shoulder joint that communicate to a customized readout electronics board through sewn micro-coaxial cables. An optimized sensor design includes passive shielding and demonstrates high linearity and low hysteresis, making it suitable for wearable motion tracking. In a study with a single human subject, we evaluated the tracking capability of the integrated shirt in comparison with a ground truth optical motion capture system. An ensemble-based regression algorithm was implemented in post-processing to estimate joint angles and angular velocities from the strain sensor data. Results demonstrated root mean square errors (RMSEs) less than 4.5° for joint angle estimation and normalized root mean square errors (NRMSEs) less than 4% for joint velocity estimation. Furthermore, we applied a recursive feature elimination (RFE)-based sensor selection analysis to down select the number of sensors for future shirt designs. This sensor selection analysis found that 5 sensors out of 8 were sufficient to generate comparable accuracies.

Autonomous robots can rely on attention mechanisms to explore complex scenes and select salient stimuli relevant for behaviour. Stimulus selection should be fast to efficiently allocate available (and limited) computational resources to process in detail a subset of the otherwise overwhelmingly large sensory input. The amount of processing required is a product of the amount of data sampled by a robot's sensors; while a standard RGB camera produces a fixed amount of data for every pixel of the sensor, an event-camera produces data only for where there is a contrast change in the field of view, and does so with a lower latency. In this paper, we describe the implementation of a state-of-the-art bottom-up attention model, based on structuring the visual scene in terms of proto-objects. As an event-camera encodes different visual information compared to frame-based cameras, the original algorithm must be adapted and modified. We find that the event-camera's inherent detection of edges removes the need for some early stages of processing in the model. We describe the modifications, compare the event-driven algorithm to the original, and validate the potential for use on the iCub humanoid robot.