A data-driven intelligent decision support system that combines predictive and prescriptive analytics for the design of new textile fabrics

In this paper, we propose an Intelligent Decision Support System (IDSS) for the design of new textile fabrics. The IDSS uses predictive analytics to estimate fabric properties (e.g., elasticity) and composition values (% cotton) and then prescriptive techniques to optimize the fabric design inputs that feed the predictive models (e.g., types of yarns used). Using thousands of data records from a Portuguese textile company, we compared two distinct Machine Learning (ML) predictive approaches: Single-Target Regression (STR), via an Automated ML (AutoML) tool, and Multi-target Regression, via a deep learning Artificial Neural Network. For the prescriptive analytics, we compared two Evolutionary Multi-objective Optimization (EMO) methods (NSGA-II and R-NSGA-II) when optimizing 100 new fabrics, aiming to simultaneously minimize the physical property predictive error and the distance of the optimized values when compared with the learned input space. The two EMO methods were applied to design of 100 new fabrics. Overall, the STR approach provided the best results for both prediction tasks, with Normalized Mean Absolute Error values that range from 4% (weft elasticity) to 11% (pilling) in terms of the fabric properties and a textile composition classification accuracy of 87% when adopting a small tolerance of 0.01 for predicting the percentages of six types of fibers (e.g., cotton). As for the prescriptive results, they favored the R-NSGA-II EMO method, which tends to select Pareto curves that are associated with an average 11% predictive error and 16% distance.This work was carried out within the project "TexBoost: less Commodities more Specialities" reference POCI-01-0247-FEDER-024523, co-funded by Fundo Europeu de Desenvolvimento Regional (FEDER), through Portugal 2020 (P2020)

Deep autoencoders for acoustic anomaly detection: experiments with working machine and in-vehicle audio

The growing usage of digital microphones has generated an increased interest in the topic of Acoustic Anomaly Detection (AAD). Indeed, there are several real-world AAD application domains, including working machines and in-vehicle intelligence (the main target of this research project). This paper introduces three deep AutoEncoders (AE) for unsupervised AAD tasks, namely a Dense AE, a Convolutional Neural Network (CNN) AE and Long Short-Term Memory Autoencoder (LSTM) AE. To tune the deep learning architectures, development data were adopted from public domain audio datasets related with working machines. A large set of computational experiments was held, showing that the three proposed deep autoencoders, when combined with a melspectrogram sound preprocessing, are quite competitive and outperform a recently proposed AE baseline. Next, on a second experimental stage, aiming to address the final in-vehicle passenger safety goal, the three AEs were adapted to learn from in-vehicle normal audio, assuming three realistic scenarios that were generated by a synthetic audio mixture tool. In general, a high quality AAD discrimination was obtained: working machine data - 72% to 91%; and in-vehicle audio - 78% to 81%. In conjunction with an automotive company, an in-vehicle AAD intelligent system prototype was further developed, aiming to test a selected model (LSTM AE) during a pilot demonstration event that targeted the cough anomaly. Interesting results were obtained, with the AAD system presenting a high cough classification accuracy (e.g., 100% for front seat locations).This work is supported by the European Structural and Investment Funds in the FEDER component, through the Operational Competitiveness and Internationalization Programme (COMPETE 2020) - Project no 039334; Funding Reference: POCI-01-0247-FEDER-039334

A Deep Learning approach to prevent problematic movements of industrial workers based on inertial sensors

Nowadays, manufacturing industries still face difficulties applying traditional Work-related MusculoSkeletal Disorders (WMSDs) risk assessment methods due to the high effort required by a continuous data collection when using observational methods. An interesting solution is to adopt Inertial Measurement Units (IMUs) to automate the data collection, thus supporting occupational health professionals. In this paper, we propose a deep learning approach to predict human motion based on IMU data with the goal of preventing industrial worker problematic movements that can arise during repetitive actions. The proposed system includes an initial Madgwick filter to merge the raw inertial tri-axis sensor data into a single angle orientation time series. Then, a Machine Learning (ML) algorithm is trained with the obtained time series, allowing to build a forecasting model. The effectiveness of the developed system was validated by using an open-source dataset composed of different motions for the upper body collected in a laboratory environment, aiming to monitor the abduction/adduction angle of the arm. Firstly, distinct ML algorithms were compared for a single angle orientation time series prediction, including: three Long Short-Term Memory (LSTM) methods - a one layer, a stacked layer and a Sequence to Sequence (Seq2Seq) model; and three non deep learning methods - a Multiple Linear Regression, a Random Forest and a Support Vector Machine. The best results were provided by the Seq2Seq LSTM model, which was further evaluated for WMSD prevention by considering 11 human subject datasets and two evaluation procedures (single person and multiple person training and testing). Overall, interesting results were achieved, particularly for multiple person evaluation, where the proposed Seq2Seq LSTM has shown an excellent capability to anticipate problematic movements.This article is a result of the project STVgoDigital - Digitalization of the T&C sector (POCI-01-0247-FEDER-046086), supported by COMPETE 2020, under the PORTUGAL 2020 Partnership Agreement, through the European Regional De velopment Fund (ERDF)

International revenue share fraud prediction on the 5G edge using federated learning

Edge computing and multi-access edge computing (MEC) are two recent paradigms of distributed computing that are growing due to the rise of the fifth-generation (5G) of broadband cellular networks. The development of edge computing and MEC architectures involves the hosting of applications close to the end-users, allowing: an improved privacy, given that critical data is not shared with other systems; a reduced communication latency; an improved application speed; and a more efficient energy use. However, many applications are challenged by edge computing and MEC. In the case of machine learning (ML) applications, there can be privacy rules that do not allow data to be shared among distinct edges. Additionally, the devices used to train ML models might present lower computational capabilities than traditional computers. In this work, we present a Federated ML architecture that uses decentralized data and light ML training techniques to fit ML models on the 5G Edge. Our system consists of edge nodes that train models using local data and a centralized node that aggregates the results. As a case study, an international revenue share fraud task is addressed by considering two real-world datasets obtained from a commercial provider of Telecom analytics solutions. We test our architecture using two iterations of a Federated ML method, then compare it with a centralized ML model that is currently adopted by the provider. The results show that the Federated Learning decentralized approach produces an excellent level of class discrimination and that the main models maintain the performance across two rounds of decentralized training and even surpass the existing centralized model. After validating the results with the Telecom provider, we have built a prototype technological architecture that can be deployed in a real-world MEC scenario.This work was executed under the project Opti-Edge: 5G Digital Services Optimization at the Edge, Individual Project, NUP: POCI-01-0247-FEDER-045220, co-funded by the Incentive System for Research and Technological Development, from the Thematic Operational Program Competitiveness of the national framework program - Portugal2020. We wish to thank the anonymous reviewers for their helpful comments