Sunspot Time Series Forecasting using Deep Learning

In order to forecast solar cycle 25, sunspot numbers(SSN) from 1700 ∼ 2018 was used as a time series to predict the next eleven years. deep long short-term memory(LSTM) was exploited to do the forecast, first the dataset was split into training set(80%) and (20%) for the test set, the achieved accuracy led us to forecast the next eleven years. The result shows that the cycle will be from 2019 ∼ 2029 with peak at 2024

Deep Learning for Processing Electromyographic Signals: a Taxonomy-based Survey

Deep Learning (DL) has been recently employed to build smart systems that perform incredibly well in a wide range of tasks, such as image recognition, machine translation, and self-driving cars. In several fields the considerable improvement in the computing hardware and the increasing need for big data analytics has boosted DL work. In recent years physiological signal processing has strongly benefited from deep learning. In general, there is an exponential increase in the number of studies concerning the processing of electromyographic (EMG) signals using DL methods. This phenomenon is mostly explained by the current limitation of myoelectric controlled prostheses as well as the recent release of large EMG recording datasets, e.g. Ninapro. Such a growing trend has inspired us to seek and review recent papers focusing on processing EMG signals using DL methods. Referring to the Scopus database, a systematic literature search of papers published between January 2014 and March 2019 was carried out, and sixty-five papers were chosen for review after a full text analysis. The bibliometric research revealed that the reviewed papers can be grouped in four main categories according to the final application of the EMG signal analysis: Hand Gesture Classification, Speech and Emotion Classification, Sleep Stage Classification and Other Applications. The review process also confirmed the increasing trend in terms of published papers, the number of papers published in 2018 is indeed four times the amount of papers published the year before. As expected, most of the analyzed papers (≈60 %) concern the identification of hand gestures, thus supporting our hypothesis. Finally, it is worth reporting that the convolutional neural network (CNN) is the most used topology among the several involved DL architectures, in fact, the sixty percent approximately of the reviewed articles consider a CNN

Astronomia ex machina: a history, primer, and outlook on neural networks in astronomy

In recent years, deep learning has infiltrated every field it has touched, reducing the need for specialist knowledge and automating the process of knowledge discovery from data. This review argues that astronomy is no different, and that we are currently in the midst of a deep learning revolution that is transforming the way we do astronomy. We trace the history of astronomical connectionism from the early days of multilayer perceptrons, through the second wave of convolutional and recurrent neural networks, to the current third wave of self-supervised and unsupervised deep learning. We then predict that we will soon enter a fourth wave of astronomical connectionism, in which finetuned versions of an all-encompassing 'foundation' model will replace expertly crafted deep learning models. We argue that such a model can only be brought about through a symbiotic relationship between astronomy and connectionism, whereby astronomy provides high quality multimodal data to train the foundation model, and in turn the foundation model is used to advance astronomical research.Comment: 60 pages, 269 references, 29 figures. Review submitted to Royal Society Open Science. Comments and feedback welcom

Domain Knowledge Infusion in Machine Learning for Digital Signal Processing Applications : An in-depth case study on table tennis stroke recognition

Diese Arbeit untersucht die Infusion von Domänenwissen als eine Möglichkeit zur Optimierung von Anwendungen des maschinellen Lernens in der Signalverarbeitung. Als Anwendungsbeispiel wird die Erkennung von Tischtennisschlägen anhand von Signalen detailliert analysiert. Die Signale stammen von Sensoren, die in einer am Handgelenk getragenen Smartwatch integriert sind. Domänenwissen wird auf verschiedenen Abstraktionsebenen verwendet, um die Schlagerkennung und -klassifikation zu verbessern. Diese reichen von der Wahl und Fusion tischtennisrelevanter Sensoren, über Low-Level-Signalkorrekturen, bis hin zu Zustandsautomaten, die basierend auf dem Wissen über gültige Schlagsequenzen eine Selbstkorrektur von Fehlklassifikationen ermöglichen. Die Evaluation des LSTMbasierten Prototyps zeigt, dass er erfolgreich zwischen Spiel/kein Spiel, Schlag/kein Schlag, und acht Schlagarten (Vorhand/Rückhand Konter, Topspin, Block, Unterschnitt) unterscheiden kann, sowie Metriken zukünftiger Schläge zur Analyse des Spielstils basierend auf vergangenen Schlägen vorhersagen kann. Das System wurde basierend auf 3770 Schlägen von zwei langjährigen Tischtennisspielern entwickelt und validiert. Die Daten wurden in einer kontrollierten Umgebung unter Zuhilfenahme eines Tischtennisroboters gesammelt, der Bälle präzise servieren kann

Distributed Collaborative Prognostics

Managing large fleets of machines in a cost-effective way is becoming more important as corporations own increasingly large amounts of assets. The steady improvement in cost and reliability of sensors, processors and communication devices has helped the spread of a new paradigm: the Internet of Things. This paradigm allows for real-time monitoring of countless physical objects, obtaining data that can be fed to machine learning algorithms to predict their future state and take managerial decisions. Despite rapid technological change, industries have been slow to react, and it has been only recently that many have transitioned towards a new business model: servitisation. Servitisation is based on selling the services that assets provide, instead of the assets themselves. Although more companies are adopting this business model, there is a lack of solutions aimed to maximise its economic value. This thesis presents one such solution capable of predicting failures in real time, thus reducing a crucial cost contribution to asset ownership: unexpected failures. This new approach, Distributed Collaborative Prognostics, consists of providing each machine with its own particular agent, that enables it to communicate with other similar machines in order to improve its failure predictions. This thesis implements Distributed Collaborative Prognostics in three different scenarios: (i) using a multi-agent simulation framework, (ii) using synthetic data from a well-established prognostics data set, and (iii) using real data from a fleet of industrial gas turbines. Each of these scenarios is used to study different elements of the prognostics problem. Multi-agent simulations allow for the calculation of the cost of predictive maintenance coupled with Distributed Collaborative Prognostics, and for the estimation of the cost of agent failures in different architectures. Synthetic data is used as a test bench and to study assets operating in dynamic situations. Real industrial data from the Siemens industrial gas turbine fleet serves to test the applicability of the tool in a real scenario. This thesis concludes that Distributed Collaborative Prognostics is the adequate solution for large and heterogeneous fleets of assets operating dynamically. Its cost effectiveness depends on the value of the assets; in general, highly-valued assets are more conducive to Distributed Collaborative Prognostics, as the savings from improved failure predictions compensate the cost of enabling them with Internet of Things technologies.This PhD Thesis has been supported by a “la Caixa" Fellowship (ID 100010434), with code LCF/BQ/EU17/11590049

Introduction to Transformers: an NLP Perspective

Transformers have dominated empirical machine learning models of natural language processing. In this paper, we introduce basic concepts of Transformers and present key techniques that form the recent advances of these models. This includes a description of the standard Transformer architecture, a series of model refinements, and common applications. Given that Transformers and related deep learning techniques might be evolving in ways we have never seen, we cannot dive into all the model details or cover all the technical areas. Instead, we focus on just those concepts that are helpful for gaining a good understanding of Transformers and their variants. We also summarize the key ideas that impact this field, thereby yielding some insights into the strengths and limitations of these models.Comment: 119 pages and 21 figure