Manufacturing Data Analytics for Manufacturing Quality Assurance

The authors acknowledge the European Commission for the support and funding under the scope of Horizon2020 i4Q Innovation Project (Agreement Number 958205) and the remaining partners of the i4Q Project Consortium.Nowadays, manufacturing companies are eager to access insights from advanced analytics, without requiring them to have specialized IT workforce or data science advanced skills. Most of current solutions lack of easy-to-use advanced data preparation, production reporting and advanced analytics and prediction. Thanks to the increase in the use of sensors, actuators and instruments, European manufacturing lines collect a huge amount of data during the manufacturing process, which is very valuable for the improvement of quality in manufacturing, but analyzing huge amounts of data on a daily basis, requires heavy statistical and technology training and support, making them not accessible for SMEs. The European i4Q Project, aims at providing an IoT-based Reliable Industrial Data Services (RIDS), a complete suite consisting of 22 i4Q Solutions, able to manage the huge amount of industrial data coming from cheap cost-effective, smart, and small size interconnected factory devices for supporting manufacturing online monitoring and control. This paper will present a set of i4Q services, for data integration and fusion, data analytics and data distribution. Such services, will be responsible for the execution of AI workloads (including at the edge), enabling the dynamic deployment industrial scenarios based on a cloud/edge architecture. Monitoring at various levels is provided in i4Q through scalable tools and the collected data, is used for a variety of activities including resource monitoring and management, workload assignment, smart alerting, predictive failure and model (re)training.publishersversionpublishe

A Review of Multisensor Data Fusion Solutions in Smart Manufacturing: Systems and Trends

Manufacturing companies increasingly become “smarter” as a result of the Industry 4.0 revolution. Multiple sensors are used for industrial monitoring of machines and workers in order to detect events and consequently improve the manufacturing processes, lower the respective costs, and increase safety. Multisensor systems produce big amounts of heterogeneous data. Data fusion techniques address the issue of multimodality by combining data from different sources and improving the results of monitoring systems. The current paper presents a detailed review of state-of-the-art data fusion solutions, on data storage and indexing from various types of sensors, feature engineering, and multimodal data integration. The review aims to serve as a guide for the early stages of an analytic pipeline of manufacturing prognosis. The reviewed literature showed that in fusion and in preprocessing, the methods chosen to be applied in this sector are beyond the state-of-the-art. Existing weaknesses and gaps that lead to future research goals were also identified

Statistical methods for studying the variability and correlation structures of biomedical data

Statistical science is inextricably linked to the biological sciences, where it is used as a tool to help draw conclusions but also to understand diseases and biological functions in general. Clinical trials and survival models are commonly described as biostatistical methods. For many years, however, a plethora of other statistical methods have been applied to medical and biological data.The general area of research of this dissertation is the application of statistical methods to biological data. More specifically, we tried to use methods that were not widely used in similar data in order to discover homogeneous data groups, to study the correlation structures of different groups, and for prediction. The data used can be grouped into three categories: a) gene expression for genes involved in a disease, b) EEG signals for the human brain's response to a stimulus, and c) sensor measurements, which record the movements of people in order to recognize activities. The statistical methods that were the main object of study of this dissertation are the Archetypal analysis, Structural Equation Models and comparisons of correlations. Archetypal Analysis is a method that has not received the proper attention and groups observations based on their relationship to archetypes. Archetypes are some extreme cases that can be real observations. Structural Equation Models and the comparison of correlation coefficients, study correlation or covariance structures. Structural Equations Models belong to the field of multivariate analysis and have the ability to process many equations simultaneously. In this way they can treat a variable as independent in an equation and as dependent in another equation. Comparison of correlations involves some statistical tests that examine the statistically significant difference between two correlation coefficients. It is a method that is not often used, but it is very useful in the study of heterogeneous correlation structures.Archetypal Analysis has been applied to neurological responses describing the brain activity of individuals exposed to a stimulus, for grouping them into homogeneous groups, and to clinical and biological data of patients who have undergone assisted reproduction therapy to identify which hormones contribute to the success of therapy. Structural Equation Models have been used to detect gene signaling pathways in patients with Chronic Lymphocytic Leukemia. This patient cohort consisted of two groups of patients with different clinical picture. Using Structural Equation Models, most relationships between genes were modeled and differences were identified in the signaling pathways of the two patient groups. The study of correlation structures was used in both gene expression and measurement of wearable sensors to identify activity. Based on the comparison of correlations, we proposed a new method of selecting variables for classification algorithms.The dissertation is conceptually divided into two parts. The first part concerns data grouping and includes Chapters 1 to 3. In these chapters the method of Archetypal Analysis is revised and its two applications are described. The second part deals with the study of correlation and covariance structures, which was the main part of the research and consists of Chapters 4 to 9. Two chapters describe the theory of Structural Equation Models and methods of comparing correlation coefficients and the rest concern the applications of these methods.We could summarize the main points of the dissertation's contribution to the following: a) the discovery of relationships between genes of heterogeneous groups of patients with Chronic Lymphocytic Leukemia, using Structural Equation Models b) the suggestion of an alternative way of grouping brain responses using Archetypal Analysis, c) the proposal of a new method of selecting variables based on the comparison of correlations of different observation groups.Η στατιστική είναι άρρηκτα συνδεδεμένη με τις βιολογικές επιστήμες, όπου χρησιμοποιείται σαν εργαλείο για να βοηθήσει στην εξαγωγή συμπερασμάτων αλλά και στην κατανόηση ασθενειών και γενικά βιολογικών λειτουργιών. Ως μέθοδοι βιοστατιστικής χαρακτηρίζονται συνήθως οι κλινικές δοκιμές και τα μοντέλα επιβίωσης. Εδώ και πολλά χρόνια όμως, μια πληθώρα στατιστικών μεθόδων εφαρμόζεται σε ιατρικά και βιολογικά δεδομένα.Το γενικό πεδίο έρευνας της παρούσας διατριβής είναι η εφαρμογή στατιστικών μεθόδων σε βιολογικά δεδομένα. Πιο συγκεκριμένα, προσπαθήσαμε να αξιοποιήσουμε μεθόδους που δεν είχαν χρησιμοποιηθεί ευρέως σε παρόμοια δεδομένα με σκοπό την ανακάλυψη ομοιογενών ομάδων δεδομένων, την μελέτη δομών συσχέτισης διαφορετικών ομάδων αλλά και την πρόβλεψη. Τα δεδομένα που χρησιμοποιήθηκαν μπορούν να ομαδοποιηθούν σε τρεις κατηγορίες: α) εκφράσεις γονιδίων που αφορούν γονίδια που εμπλέκονται σε κάποια ασθένεια, β) ηλεκτροεγκεφαλικά σήματα που αφορούν την απόκριση του ανθρώπινου εγκεφάλου σε ερέθισμα και γ) μετρήσεις αισθητήρων, οι οποίοι καταγράφουν τις κινήσεις ανθρώπων με σκοπό την αναγνώριση δραστηριότητας. Οι στατιστικές μέθοδοι που αποτέλεσαν το βασικό αντικείμενο μελέτης αυτής της διατριβής είναι η Αρχετυπική Ανάλυση, τα Μοντέλα Δομικών Εξισώσεων και οι συγκρίσεις συσχετίσεων. Η Αρχετυπική Ανάλυση είναι μια μέθοδος που δεν έχει λάβει την πρέπουσα προσοχή και ομαδοποιεί παρατηρήσεις με βάση τη σχέση τους με τα αρχέτυπα. Τα αρχέτυπα είναι κάποιες ακραίες περιπτώσεις που μπορεί να είναι και πραγματικές παρατηρήσεις. Τα Μοντέλα Δομικών Εξισώσεων και η σύγκριση συντελεστών συσχέτισης μελετούν δομές συσχέτισης ή συνδιακύμανσης. Τα Μοντέλα Δομικών Εξισώσεων ανήκουν στον κλάδο της πολυμεταβλητής ανάλυσης και έχουν τη δυνατότητα να επεξεργάζονται ταυτόχρονα πολλές εξισώσεις. Με αυτό τον τρόπο μπορούν να αντιμετωπίζουν μια μεταβλητή σαν ανεξάρτητη σε μια εξίσωση και σαν εξαρτημένη σε άλλη εξίσωση. Η σύγκριση συσχετίσεων αφορά κάποιους ελέγχους που εξετάζουν τη στατιστικά σημαντική διαφορά ανάμεσα σε δύο συντελεστές συσχέτισης. Είναι μια μέθοδος που δεν χρησιμοποιείται συχνά, χρησιμεύει όμως πολύ στη μελέτη ετερογενών δομών συσχέτισης.Η Αρχετυπική Ανάλυση εφαρμόστηκε σε νευρολογικές αποκρίσεις που περιγράφουν την εγκεφαλική διέγερση ατόμων που εκτίθενται σε κάποιο ερέθισμα, με σκοπό την ομαδοποίηση τους σε ομογενείς ομάδες και σε κλινικά και βιολογικά δεδομένα ασθενών που υποβλήθηκαν σε υποβοηθούμενη αναπαραγωγή, για να εντοπίσει ποιες ορμόνες συνέβαλλαν στην επιτυχία της θεραπείας. Τα Μοντέλα Δομικών Εξισώσεων χρησιμοποιήθηκαν για να ανακαλυφθούν μονοπάτια γονιδιακής σηματοδότησης ασθενών με Χρόνια Λεμφοκυτταρική Λευχαιμία. Το συγκεκριμένο σύνολο ασθενών αποτελούνταν από δύο ομάδες ασθενών με διαφορετική κλινική εικόνα. Με τη χρήση των Μοντέλων Δομικών Εξισώσεων μοντελοποιήθηκαν οι περισσότερες σχέσεις ανάμεσα στα γονίδια και εντοπίστηκαν διαφορές στα μοντέλα σηματοδότησης των δύο ομάδων ασθενών. Η μελέτη των δομών συσχετίσεων χρησιμοποιήθηκε και σε εκφράσεις γονιδίων αλλά και σε μετρήσεις φορετών αισθητήρων με σκοπό την αναγνώριση δραστηριότητας. Με βάση τη σύγκριση συσχετίσεων προτείναμε μια καινούργια μέθοδο επιλογής μεταβλητών για αλγορίθμους ταξινόμησης.Η διατριβή χωρίζεται νοητά σε δύο μέρη. Το πρώτο μέρος αφορά την ομαδοποίηση δεδομένων και περιλαμβάνει τα Κεφάλαια 1 ως 3. Σε αυτά αναπτύσσεται η μέθοδος της Αρχετυπικής Ανάλυσης και περιγράφονται οι δύο εφαρμογές της. Το δεύτερο μέρος αφορά τη μελέτη δομών συσχέτισης και συνδιακύμανσης, που αποτέλεσε και το κύριο μέρος της έρευνας και αποτελείται από τα Κεφάλαια 4 ως 9. Δύο κεφάλαια περιγράφουν τη θεωρία για τα Μοντέλα Δομικών Εξισώσεων και τις μεθόδους σύγκρισης συντελεστών συσχέτισης και τα υπόλοιπα αφορούν εφαρμογές αυτών των μεθόδων.Θα μπορούσαμε να συνοψίσουμε τα κύρια σημεία της συνεισφοράς της διατριβής στα παρακάτω: α) την ανακάλυψη σχέσεων ανάμεσα σε γονίδια ετερογενών ομάδων ασθενών με Χρόνια Λεμφοκυτταρική Λευχαιμία, με τη χρήση των Μοντέλων Δομικών Εξισώσεων β) προτάθηκε ένας εναλλακτικός τρόπος ομαδοποίησης εγκεφαλικών αποκρίσεων με χρήση της Αρχετυπικής Ανάλυσης, γ) προτάθηκε μια καινούργια μέθοδος επιλογής μεταβλητών που βασίζεται στη σύγκριση συσχετίσεων διαφορετικών ομάδων παρατηρήσεων

Combining RSSI and Accelerometer Features for Room-Level Localization

The continuing advancements in technology have resulted in an explosion in the use of interconnected devices and sensors. Internet-of-Things (IoT) systems are used to provide remote solutions in different domains, like healthcare and security. A common service offered by IoT systems is the estimation of a person’s position in indoor spaces, which is quite often achieved with the exploitation of the Received Signal Strength Indication (RSSI). Localization tasks with the goal to locate the room are actually classification problems. Motivated by a current project, where there is the need to locate a missing child in crowded spaces, we intend to test the added value of using an accelerometer along with RSSI for room-level localization and assess the performance of ensemble learning methods. We present here the results of this preliminary approach of the early and late fusion of RSSI and accelerometer features in room-level localization. We further test the performance of the feature extraction from RSSI values. The classification algorithms and the fusion methods used to predict the room were evaluated using different protocols applied to a public dataset. The experimental results revealed better performance of the RSSI extracted features, while the accelerometer’s individual performance was poor and subsequently affected the fusion results

Graph Theoretical Analysis of EEG Functional Connectivity Patterns and Fusion with Physiological Signals for Emotion Recognition

Emotion recognition is a key attribute for realizing advances in human–computer interaction, especially when using non-intrusive physiological sensors, such as electroencephalograph (EEG) and electrocardiograph. Although functional connectivity of EEG has been utilized for emotion recognition, the graph theory analysis of EEG connectivity patterns has not been adequately explored. The exploitation of brain network characteristics could provide valuable information regarding emotions, while the combination of EEG and peripheral physiological signals can reveal correlation patterns of human internal state. In this work, a graph theoretical analysis of EEG functional connectivity patterns along with fusion between EEG and peripheral physiological signals for emotion recognition has been proposed. After extracting functional connectivity from EEG signals, both global and local graph theory features are extracted. Those features are concatenated with statistical features from peripheral physiological signals and fed to different classifiers and a Convolutional Neural Network (CNN) for emotion recognition. The average accuracy on the DEAP dataset using CNN was 55.62% and 57.38% for subject-independent valence and arousal classification, respectively, and 83.94% and 83.87% for subject-dependent classification. Those scores went up to 75.44% and 78.77% for subject-independent classification and 88.27% and 90.84% for subject-dependent classification using a feature selection algorithm, exceeding the current state-of-the-art results

Assessing Virtual Reality Spaces for Elders Using Image-Based Sentiment Analysis and Stress Level Detection

Seniors, in order to be able to fight loneliness, need to communicate with other people and be engaged in activities to keep their minds active to increase their social capital. There is an intensified interest in the development of social virtual reality environments, either by commerce or by academia, to address the problem of social isolation of older people. Due to the vulnerability of the social group involved in this field of research, the need for the application of evaluation methods regarding the proposed VR environments becomes even more important. The range of techniques that can be exploited in this field is constantly expanding, with visual sentiment analysis being a characteristic example. In this study, we introduce the use of image-based sentiment analysis and behavioural analysis as a technique to assess a social VR space for elders and present some promising preliminary results

A Multimodal Late Fusion Framework for Physiological Sensor and Audio-Signal-Based Stress Detection: An Experimental Study and Public Dataset

Stress can be considered a mental/physiological reaction in conditions of high discomfort and challenging situations. The levels of stress can be reflected in both the physiological responses and speech signals of a person. Therefore the study of the fusion of the two modalities is of great interest. For this cause, public datasets are necessary so that the different proposed solutions can be comparable. In this work, a publicly available multimodal dataset for stress detection is introduced, including physiological signals and speech cues data. The physiological signals include electrocardiograph (ECG), respiration (RSP), and inertial measurement unit (IMU) sensors equipped in a smart vest. A data collection protocol was introduced to receive physiological and audio data based on alterations between well-known stressors and relaxation moments. Five subjects participated in the data collection, where both their physiological and audio signals were recorded by utilizing the developed smart vest and audio recording application. In addition, an analysis of the data and a decision-level fusion scheme is proposed. The analysis of physiological signals includes a massive feature extraction along with various fusion and feature selection methods. The audio analysis comprises a state-of-the-art feature extraction fed to a classifier to predict stress levels. Results from the analysis of audio and physiological signals are fused at a decision level for the final stress level detection, utilizing a machine learning algorithm. The whole framework was also tested in a real-life pilot scenario of disaster management, where users were acting as first responders while their stress was monitored in real time

Internet of Things Infrastructure for Security and Safety in Public Places

We present the technologies and the theoretical background of an intelligent interconnected infrastructure for public security and safety. The innovation of the framework lies in the intelligent combination of devices and human information towards human and situational awareness, so as to provide a protection and security environment for citizens. The framework is currently being used to support visitors in public spaces and events, by creating the appropriate infrastructure to address a set of urgent situations, such as health-related problems and missing children in overcrowded environments, supporting smart links between humans and entities on the basis of goals, and adapting device operation to comply with human objectives, profiles, and privacy. State-of-the-art technologies in the domain of IoT data collection and analytics are combined with localization techniques, ontologies, reasoning mechanisms, and data aggregation in order to acquire a better understanding of the ongoing situation and inform the necessary people and devices to act accordingly. Finally, we present the first results of people localization and the platforms’ ontology and representation framework

Smart Interconnected Infrastructures for Security and Protection: The DESMOS Project

This paper presents “DESMOS”, a novel ecosystem for the interconnection of smart infrastructures, mobile and wearable devices, and applications, to provide a secure environment for visitors and tourists. The presented solution brings together state-of-the-art IoT technologies, crowdsourcing, localization through BLE, and semantic reasoning, following a privacy and security-by-design approach to ensure data anonymization and protection. Despite the COVID-19 pandemic, the solution was tested, validated, and evaluated via two pilots in almost real settings—involving a fewer density of people than planned—in Trikala, Thessaly, Greece. The results and findings support that the presented solutions can provide successful emergency reporting, crowdsourcing, and localization via BLE. However, these results also prompt for improvements in the user interface expressiveness, the application’s effectiveness and accuracy, as well as evaluation in real, overcrowded conditions. The main contribution of this paper is to report on the progress made and to showcase how all these technological solutions can be integrated and applied in realistic and practical scenarios, for the safety and privacy of visitors and tourists

Connecting the Elderly Using VR: A Novel Art-Driven Methodology

Demographic change confronts us with an ever-increasing number of elderly people who face isolation and socialization issues. Background: The main challenge of this study is to inject emotional and aesthetic aspects into the design process of a virtual reality (VR) social space for the elderly. In this context, we asked architects and artists to improve the perception elderly people have of their way of communicating with others. Artists, in collaboration with computer engineers, designed experiences that evoke positive cognitive and emotional feelings and memories by following design trends and aesthetic values likely to be appreciated by older people, which were integrated in VR. Methods: We approached our goal by implementing an innovative art-driven methodology, using a plethora of technologies and methods, such as VR, artificial intelligence algorithms, visual analysis, and 3D mapping, in order to make design decisions based on a detailed understanding of the users’ preferences and collective behavior. Results: A so-called virtual village “Cap de Ballon” was co-created, having a public space inspired by the villages of Santorini and Meteora and a private space inspired by the 3D scanning of an elderly person’s apartment. Conclusions: The overall concept of the VR village‘s utility, design, and interior design were appreciated by the end users and the concept was evaluated as original and stimulating for creativity