Improving accuracy on wave height estimation through machine learning techniques

Estimatabion of wave agitation plays a key role in predicting natural disasters, path optimization and secure harbor operation. The Spanish agency Puertos del Estado (PdE) has several oceanographic measure networks equipped with sensors for different physical variables, and manages forecast systems involving numerical models. In recent years, there is a growing interest in wave parameter estimation by using machine learning models due to the large amount of oceanographic data available for training, as well as its proven efficacy in estimating physical variables. In this study, we propose to use machine learning techniques to improve the accuracy of the current forecast system of PdE. We have focused on four physical wave variables: spectral significant height, mean spectral period, peak period and mean direction of origin. Two different machine learning models have been explored: multilayer perceptron and gradient boosting decision trees, as well as ensemble methods that combine both models. These models reduce the error of the predictions of the numerical model by 36% on average, demonstrating the potential gains of combining machine learning and numerical models

Analysis of Li-ion battery degradation using self-organizing maps

This paper proposes a new methodology to identify the different degradation processes of Li-Ion battery cells. The goal of this study is to determine if different degradation factors can be separated by waveform analysis from aged cells with similar remaining capacity. In contrast to other works, the proposed method identifies the past operating conditions in the cell, regardless of the actual State of Health. The methodology is based on a data-driven approach by using a SOM (Self-organizing map), an unsupervised neural network. To verify the hypothesis a SOM has been trained with laboratory data from whole data cycles, to classify cells concerning their degradation path and according to their discharge voltage patterns. Additionally, this new methodology based on the SOM allows discriminating groups of cells with different cycling conditions (based on depth of discharge, ambient temperature and discharge current). This research line is very promising for classification of used cells, not only depending on their current static parameters (capacity, impedance), but also the battery use in their past life. This will allow making predictions of the Remaining Useful Life (RUL) of a battery with greater precision

One Rule to Rule Them All? Organisational Sensemaking of Corporate Responsibility

Corporate responsibility (CR) has often been criticised as a decoupled organisational phenomenon: a publicly espoused rule that is not followed in daily organisational practices. We argue that a crucial reason for this criticism arises from the dominant in-house assumption of CR literature, which mitigates tensions and contradictions in organisational life by claiming that integrated rules result in coupled practices. We aim to provide new insights by problematising this in-house assumption and by examining how members of two organisations discursively make sense of CR, as a daily rule-bound practice, via three strategies: integration, differentiation and fragmentation. We elaborate the contemporary literature on CR as a daily organisational practice by examining the significance of discursive sensemaking for organisational rules for further development and learning regarding CR. We then discuss the significance of our results for understanding CR as a coupled/decoupled phenomenon.peerReviewe

The DUNE far detector vertical drift technology Technical design report

DUNE is an international experiment dedicated to addressing some of the questions at the forefront of particle physics and astrophysics, including the mystifying preponderance of matter over antimatter in the early universe. The dual-site experiment will employ an intense neutrino beam focused on a near and a far detector as it aims to determine the neutrino mass hierarchy and to make high-precision measurements of the PMNS matrix parameters, including the CP-violating phase. It will also stand ready to observe supernova neutrino bursts, and seeks to observe nucleon decay as a signature of a grand unified theory underlying the standard model. The DUNE far detector implements liquid argon time-projection chamber (LArTPC) technology, and combines the many tens-of-kiloton fiducial mass necessary for rare event searches with the sub-centimeter spatial resolution required to image those events with high precision. The addition of a photon detection system enhances physics capabilities for all DUNE physics drivers and opens prospects for further physics explorations. Given its size, the far detector will be implemented as a set of modules, with LArTPC designs that differ from one another as newer technologies arise. In the vertical drift LArTPC design, a horizontal cathode bisects the detector, creating two stacked drift volumes in which ionization charges drift towards anodes at either the top or bottom. The anodes are composed of perforated PCB layers with conductive strips, enabling reconstruction in 3D. Light-trap-style photon detection modules are placed both on the cryostat's side walls and on the central cathode where they are optically powered. This Technical Design Report describes in detail the technical implementations of each subsystem of this LArTPC that, together with the other far detector modules and the near detector, will enable DUNE to achieve its physics goals