The relationship between charge distribution, charge packet formation and electroluminescence in XLPE under DC

Different reports describing the internal distribution of space charge in cross-linked polyethylene (XLPE) under DC field have been published recently. The most striking fact observed is the organization of the space charge into charge packets that cross the insulation. All models for charge packet formation imply that carrier recombination will occur. As the recombination region is potentially a luminescence one it is of interest to record the electroluminescence in this regime. This topic is addressed in this paper

Co-simulated digital twin on the network edge: A vehicle platoon

This paper presents an approach to create high-fidelity models suited for digital twin application of distributed multi-agent cyber–physical systems (CPSs) exploiting the combination of simulation units through co-simulation. This approach allows for managing the complexity of cyber–physical systems by decomposing them into multiple intertwined components tailored to specific domains. The native modular design simplifies the building, testing, prototyping, and extending CPSs compared to monolithic simulator approaches. A system of platoon of vehicles is used as a case study to show the advantages achieved with the proposed approach. Multiple components model the physical dynamics, the communication network and protocol, as well as different control software and external environmental situations. The model of the platooning system is used to compare the performance of Vehicle-to-Vehicle communication against a centralized multi-access edge computing paradigm. Moreover, exploiting the detailed model of vehicle dynamics, different road surface conditions are considered to evaluate the performance of the platooning system. Finally, taking advantage of the co-simulation approach, a solution to drive a platoon in critical road conditions has been proposed. The paper shows how co-simulation and design space exploration can be used for parameter calibration and the design of countermeasures to unsafe situations

Design and Validation of Cyber-Physical Systems Through Co-Simulation: The Voronoi Tessellation Use Case

This paper reports on the use of co-simulation techniques to build prototypes of co-operative autonomous robotic cyber-physical systems. Designing such systems involves a mission-specific planner algorithm, a control algorithm to drive an agent performing its task; and the plant model to simulate the agent dynamics. An application aimed at positioning a swarm of unmanned aerial vehicles (drones) in a bounded area, exploiting a Voronoi tessellation algorithm developed in this work, is taken as a case study. The paper shows how co-simulation allows testing the complex system at the design phase using models created with different languages and tools. The paper then reports on how the adopted co-simulation platform enables control parameters calibration, by exploiting design space exploration technology. The INTO-CPS co-simulation platform, compliant with the Functional Mock-up Interface standard to exchange dynamic simulation models using various languages, was used in this work. The different software modules were written in Modelica, C, and Python. In particular, the latter was used to implement an original variant of the Voronoi algorithm to tesselate a convex polygonal region, by means of dummy points added at appropriate positions outside the bounding polygon. A key contribution of this case study is that it demonstrates how an accurate simulation of a cooperative drone swarm requires modeling the physical plant together with the high-level coordination algorithm. The coupling of co-simulation and design space exploration has been demonstrated to support control parameter calibration to optimize energy consumption and convergence time to the target positions of the drone swarm. From a practical point of view, this makes it possible to test the ability of the swarm to self-deploy in space in order to achieve optimal detection coverage and allow unmanned aerial vehicles in a swarm to coordinate with each other

Block-Based Models and Theorem Proving in Model-Based Development

This paper presents a methodology to integrate computer-assisted theorem proving into a standard workflow for model-based development that uses a block-based language as a modeling and simulation tool. The theorem prover provides confidence in the results of the analysis as it guides the developers towards a correct formalization of the system under development

on field durability tests of mechanical systems the use of the fatigue damage spectrum

Abstract: In the present paper the authors, starting from a previously proposed method for the combination and the synthesis of equivalent load conditions (by only managing PSD representations of the load conditions), developed a new approach based on the concept of Fatigue Damage Spectrum and on the system dynamics. The proposed approach was then validated by a durability test case, in which two different acceleration motion based load conditions, a norm load condition (by using laboratory test) and an operative one (by using acceleration measurements acquired during an experimental activity conducted on a transport vehicle) were compared

A human-oriented design process for collaborative robotics

The potential of collaborative robotics often does not materialize in an efficient design of the human-robot collaboration. Technology-oriented approaches are no longer enough in the Industry 4.0 era. This work proposes a set of methods to support manufacturing engineers in the human-oriented design process of integrated production systems to obtain satisfactory performance in the mass customization paradigm, without impacting the safety and health of workers. It founds the design criteria definition on five main pillars (safety, ergonomics, effectiveness, flexibility, and costs), favors the consideration of different design alternatives, and leads their selection. The dynamic impact of the design choices on the various elements of the system prevails over the static design constraints. The method has been experimented in collaboration with the major kitchen manufacturer in Italy, which introduced a collaborative robotics cell in the drawers' assembly line. It resulted in a more balanced production line (10% more), a verified risk minimization (RULA score reduced from 5 to 3 and OCRA score from 13.30 to 5.70), and a greater allocation of operators to high added value activities

Formal verification and co-simulation in the design of a synchronous motor control algorithm

Mechatronic systems are a class of cyber-physical systems, whose increasing complexity makes their validation and verification more and more difficult, while their requirements become more challenging. This paper introduces a development method based on model-based design, co-simulation and formal verification. The objective of this paper is to show the applicability of the method in an industrial setting. An application case study comes from the field of precision servo-motors, where formal verification has been used to find acceptable intervals of values for design parameters of the motor controller, which have been further explored using co-simulation to find optimal values. The reported results show that the method has been applied successfully to the case study, augmenting the current model-driven development processes by formal verification of stability, formal identification of acceptable parameter ranges, and automatic design-space exploration

An empirical evaluation of prediction by partial matching in assembly assistance systems

Industrial assistive systems result from a multidisciplinary effort that integrates IoT (and Industrial IoT), Cognetics, and Artificial Intelligence. This paper evaluates the Prediction by Partial Matching algorithm as a component of an assembly assistance system that supports factory workers, by providing choices for the next manufacturing step. The evaluation of the proposed method was performed on datasets collected within an experiment involving trainees and experienced workers. The goal is to find out which method best suits the datasets in order to be integrated afterwards into our context-aware assistance system. The obtained results show that the Prediction by Partial Matching method presents a significant improvement with respect to the existing Markov predictors

Deep Learning-Based Phase Retrieval Scheme for Minimum-Phase Signal Recovery

We propose a deep learning-based phase retrieval method to accurately reconstruct the optical field of a single-sideband minimum-phase signal from the directly detected intensity waveform. Our method relies on a fully convolutional Neural Network (NN) model to realize non-iterative and robust phase retrieval. The NN is trained so that it performs full-field reconstruction and jointly compensates for transmission impairments. Compared to the recently proposed Kramers-Kronig (KK) receiver, our method avoids the distortions introduced by the nonlinear operations involved in the KK phase-retrieval algorithm and hence does not require digital upsampling. We validate the proposed phase-retrieval method by means of extensive numerical simulations in relevant system settings, and we compare the performance of the proposed scheme with the conventional KK receiver operated with a 4-fold digital upsampling. The results show that the 7% hard-decision forward error correction (HD-FEC) threshold at BER 3.8e-3 can be achieved with up to 2.8 dB lower carrier-to-signal power ratio (CSPR) value and 1.8 dB better receiver sensitivity compared to the conventional 4-fold upsampled KK receiver. We also present a comparative analysis of the complexity of the proposed scheme with that of the KK receiver, showing that the proposed scheme can achieve the 7% HD-FEC threshold with 1.6 dB lower CSPR, 0.4 dB better receiver sensitivity, and 36% lower complexity