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Sensors and Algorithms in Industry 4.0: Security and Health Preservation Applications
Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.
2021 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Globalisation and technological digitisation have triggered an Industry 4.0. revolution.  The core of this revolution is autonomisation of complex processes, which require expert knowledge. The technical foundations of Industry 4.0 are IoT, Big Data and AI technologies. Nowadays, autonomous systems are widely used to increase human and environmental safety and to prevent health degradation.  Such non-industrial, life related applications demand high reliability as well as precision and accuracy, which challenge engineering science. 

The thesis objective is to provide suitable solutions for non-invasive, automated, and autonomous systems used for life protection and health maintenance. The proposed solutions enable non-invasive measurements by means of vision and acoustic sensors. The presented methods and systems are designed based on an analytical assessment of existing technologies and algorithms. New hardware solutions, signal and data processing methods, as well as classification and decision-making algorithms are proposed. Where required, additional customisations and modifications are applied. The systems and methods presented have been modelled and rigorously validated, and subsequently implemented and verified in a real environment.  

The scope of the thesis includes the assessment of functional requirements, precision, accuracy and reliability of life-related technological systems. It covers an analytical evaluation of proposed methods and algorithms of filtration, feature extraction, also detection, localization, identification, and classification of objects. The application fields are health monitoring, nature observation and facilitating collaborative frameworks in modern factories. 

The thesis specifically focuses on methods and algorithms of autonomous decision making concerning the risk of heart disease, the threat of fatal collision of rare birds with man-made structures and the prevention of accidents in modern robotised factories. It also deals with the implementation of the Industry 4.0 fundamentals, which are smart sensing, IoT and AI methods optimised to improve the system performance in a broad sense. The applied distributed computing method and machine-to-machine communication are aimed at limiting the data stream at an early stage of the decision-making process, and thus ensure the system’s cost-effectiveness. From the thesis, one can understand how the Industry 4.0 paradigm can contribute to autonomisation of compound processes and to increase system performance, without compromising its affordability.

The thesis is divided into two parts. The first, Prolegomena provides an overview of the sensors and algorithms applicable to industrial safety along with human health and nature preservation. This part also visualizes the relationships and interactions among the articles comprising the second part named Papers. In general, each of the enclosed six papers deals with the problem of autonomisation of complex processes in real-time and in a regular environment.

Place, publisher, year, edition, pages
Karlskrona: Blekinge Tekniska Högskola, 2021.
Series
Blekinge Institute of Technology Doctoral Dissertation Series, ISSN 1653-2090 ; 4
Keywords [en]
Acoustic Sensor, Artificial Intelligence, Autonomisation, Classification, Detection, Feature Extraction, Health Preservation, Identification, Internet of Things, Machine Learning, Multi-Sensor System, Safety System, Vision System
National Category
Engineering and Technology Signal Processing Computer Systems
Research subject
Systems Engineering
Identifiers
URN: urn:nbn:se:bth-21387ISBN: 978-91-7295-424-3 (print)OAI: oai:DiVA.org:bth-21387DiVA, id: diva2:1553796
Public defence
2021-09-10, Zoom, Campus Gräsvik, Karlskrona, 13:15 (English)
Opponent
Supervisors
Available from: 2021-05-11 Created: 2021-05-10 Last updated: 2021-09-20Bibliographically approved
List of papers
1. Wavelet-based denoising method for real phonocardiography signal recorded by mobile devices in noisy environment
Open this publication in new window or tab >>Wavelet-based denoising method for real phonocardiography signal recorded by mobile devices in noisy environment
2014 (English)In: Computers in Biology and Medicine, ISSN 0010-4825, Vol. 52, p. 119-129Article in journal (Refereed) Published
Abstract [en]

The main obstacle in development of intelligent autodiagnosis medical systems based on the analysis of phonocardiography (PCG) signals is noise. The noise can be caused by digestive and respiration sounds, movements or even signals from the surrounding environment and it is characterized by wide frequency and intensity spectrum. This spectrum overlaps the heart tones spectrum, which makes the problem of PCG signal filtrating complex. The most common method for filtering such signals are wavelet denoising algorithms. In previous studies, in order to determine the optimum wavelet denoising parameters the disturbances were simulated by Gaussian white noise. However, this paper shows that this noise has a variable character. Therefore, the purpose of this paper is adaptation of a wavelet denoising algorithm for the filtration of real PCG signal disturbances from signals recorded by a mobile devices in a noisy environment. The best results were obtained for Coif 5 wavelet at the 10th decomposition level with the use of a minimaxi threshold selection algorithm and mln rescaling function. The performance of the algorithm was tested on four pathological heart sounds: early systolic murmur, ejection click, late systolic murmur and pansystolic murmur.

Keywords
Wavelet transforms Phonocardiography (PCG) Colored noise Noise cancellation Mobile healthcare
National Category
Signal Processing
Research subject
Applied Signal Processing
Identifiers
urn:nbn:se:bth-21395 (URN)10.1016/j.compbiomed.2014.06.011 (DOI)
Available from: 2021-05-17 Created: 2021-05-17 Last updated: 2021-06-30Bibliographically approved
2. A Wavelet Transform-Based Neural Network Denoising Algorithm for Mobile Phonocardiography
Open this publication in new window or tab >>A Wavelet Transform-Based Neural Network Denoising Algorithm for Mobile Phonocardiography
2019 (English)In: Sensors, E-ISSN 1424-8220, Vol. 19, no 4, article id 957Article in journal (Refereed) Published
Abstract [en]

Cardiovascular pathologies cause 23.5% of human deaths, worldwide. An auto-diagnostic system monitoring heart activity, which can identify the early symptoms of cardiac illnesses, might reduce the death rate caused by these problems. Phonocardiography (PCG) is one of the possible techniques able to detect heart problems. Nevertheless, acoustic signal enhancement is required since it is exposed to various disturbances coming from different sources. The most common denoising enhancement is based on the Wavelet Transform (WT). However, the WT is highly susceptible to variations in the noise frequency distribution. This paper proposes a new adaptive denoising algorithm, which combines WT and Time Delay Neural Networks (TDNN). The acquired signal is decomposed by means of the WT using the coif five-wavelet basis at the tenth decomposition level and then provided as input to the TDNN. Besides the advantage of adaptive thresholding, the reason for using TDNNs is their capacity of estimating the Inverse Wavelet Transform (IWT). The best parameters of the TDNN were found for a NN consisting of 25 neurons in the first and 15 in the second layer and the delay block of 12 samples. The method was evaluated on several pathological heart sounds and on signals recorded in a noisy environment. The performance of the developed system with respect to other wavelet-based denoising approaches was validated by the online questionnaire.

Place, publisher, year, edition, pages
MDPI, 2019
Keywords
adaptive filters, auscultation techniques, auto-diagnostic system, cardiovascular pathologies, Inverse Wavelet Transform (IWT), noise cancellation, signal denoising, Time Delay Neural Networks (TDNN)
National Category
Signal Processing
Identifiers
urn:nbn:se:bth-17760 (URN)10.3390/s19040957 (DOI)000460829200208 ()30813479 (PubMedID)
Note

open access

Available from: 2019-04-04 Created: 2019-04-04 Last updated: 2022-02-10Bibliographically approved
3. A system for heart sounds classification
Open this publication in new window or tab >>A system for heart sounds classification
2014 (English)In: PLOS ONE, E-ISSN 1932-6203, Vol. 9, no 11, p. 1-12, article id e112673Article in journal (Refereed) Published
Abstract [en]

The future of quick and efficient disease diagnosis lays in the development of reliable non-invasive methods. As for the cardiac diseases – one of the major causes of death around the globe – a concept of an electronic stethoscope equipped with an automatic heart tone identification system appears to be the best solution. Thanks to the advancement in technology, the quality of phonocardiography signals is no longer an issue. However, appropriate algorithms for auto-diagnosis systems of heart diseases that could be capable of distinguishing most of known pathological states have not been yet developed. The main issue is non-stationary character of phonocardiography signals as well as a wide range of distinguishable pathological heart sounds. In this paper a new heart sound classification technique, which might find use in medical diagnostic systems, is presented. It is shown that by combining Linear Predictive Coding coefficients, used for future extraction, with a classifier built upon combining Support Vector Machine and Modified Cuckoo Search algorithm, an improvement in performance of the diagnostic system, in terms of accuracy, complexity and range of distinguishable heart sounds, can be made. The developed system achieved accuracy above 93% for all considered cases including simultaneous identification of twelve different heart sound classes. The respective system is compared with four different major classification methods, proving its reliability.

Place, publisher, year, edition, pages
Public library of science, 2014
Keywords
Heart, Heart Sounds, Humans, Phonocardiography, Reproducibility of Results, Signal Processing, Computer-Assisted, Support Vector Machines, Systolic Murmurs
National Category
Medical and Health Sciences Medical Laboratory and Measurements Technologies Signal Processing
Research subject
Applied Signal Processing; Software Engineering
Identifiers
urn:nbn:se:bth-21394 (URN)10.1371/journal.pone.0112673 (DOI)2-s2.0-84911488769 (Scopus ID)
Note

open access

Available from: 2021-05-17 Created: 2021-05-17 Last updated: 2021-06-30Bibliographically approved
4. A Distributed Computing Real-Time Safety System of Collaborative Robot
Open this publication in new window or tab >>A Distributed Computing Real-Time Safety System of Collaborative Robot
Show others...
2020 (English)In: Elektronika ir Elektrotechnika, ISSN 1392-1215, Vol. 26, no 2, p. 4-14Article in journal (Refereed) Published
Abstract [en]

Robotization has become common in modern factories due to its efficiency and cost-effectiveness. Lots of robots and manipulators share their workspaces with humans what could lead to hazardous situations causing health damage or even death. This article presents a real-time safety system applying the distributed computing paradigm for a collaborative robot. The system consists of detection/sensing modules connected with a server working as decision-making system. Each configurable sensing module pre-processes vision information and then sends to the server the images cropped to new objects extracted from a background. After identifying persons from the images, the decision-making system sends a request to the robot to perform pre-defined action. In the proposed solution, there are indicated three safety zones defined by three different actions on a robot motion. As identification method, state-of-the-art of Machine Learning algorithms, the Histogram of Oriented Gradients (HOG), Viola-Jones, and You Only Look Once (YOLO), have been examined and presented. The industrial environment tests indicated that YOLOv3 algorithm outperformed other solutions in terms of identification capabilities, false positive rate and maximum latency.

Place, publisher, year, edition, pages
Kauno Technologijos Universitetas, 2020
Keywords
Artificial intelligence, Collaborative robots, Neural networks, Safety system
National Category
Signal Processing Robotics
Identifiers
urn:nbn:se:bth-19446 (URN)10.5755/j01.eie.26.2.25757 (DOI)000529319700001 ()2-s2.0-85086876420 (Scopus ID)
Note

open access

Available from: 2020-05-14 Created: 2020-05-14 Last updated: 2023-06-07Bibliographically approved
5. A runway safety system based on vertically oriented stereovision
Open this publication in new window or tab >>A runway safety system based on vertically oriented stereovision
Show others...
2021 (English)In: Sensors, E-ISSN 1424-8220, Vol. 21, no 4, p. 1-25, article id 1464Article in journal (Refereed) Published
Abstract [en]

In 2020, over 10,000 bird strikes were reported in the USA, with average repair costs exceeding $200 million annually, rising to $1.2 billion worldwide. These collisions of avifauna with airplanes pose a significant threat to human safety and wildlife. This article presents a system dedicated to monitoring the space over an airport and is used to localize and identify moving objects. The solution is a stereovision based real-time bird protection system, which uses IoT and distributed computing concepts together with advanced HMI to provide the setup’s flexibility and usability. To create a high degree of customization, a modified stereovision system with freely oriented optical axes is proposed. To provide a market tailored solution affordable for small and medium size airports, a user-driven design methodology is used. The mathematical model is implemented and optimized in MATLAB. The implemented system prototype is verified in a real environment. The quantitative validation of the system performance is carried out using fixed-wing drones with GPS recorders. The results obtained prove the system’s high efficiency for detection and size classification in real-time, as well as a high degree of localization certainty. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.

Place, publisher, year, edition, pages
MDPI, 2021
Keywords
Bird monitoring, Bird strike, Distributed computing, Environmental sustainability, Internet of Things, Localization, Monitoring of avifauna, Runway safety, Safety system, Stereovision, Visual sensor network, Birds, Fixed wings, MATLAB, Bird protection systems, Degree of localization, Design Methodology, Quantitative validation, Real environments, Size classification, Stereo-vision system, Tailored Solutions, Aircraft accidents
National Category
Robotics Signal Processing
Identifiers
urn:nbn:se:bth-21173 (URN)10.3390/s21041464 (DOI)000624684500001 ()2-s2.0-85100917246 (Scopus ID)
Note

open access

Available from: 2021-03-04 Created: 2021-03-04 Last updated: 2023-06-07Bibliographically approved
6. Comprehensive bird preservation at wind farms
Open this publication in new window or tab >>Comprehensive bird preservation at wind farms
Show others...
2021 (English)In: Sensors, E-ISSN 1424-8220, Vol. 21, no 1, p. 1-35, article id 267Article in journal (Refereed) Published
Abstract [en]

Wind as a clean and renewable energy source has been used by humans for centuries. However, in recent years with the increase in the number and size of wind turbines, their impact on avifauna has become worrisome. Researchers estimated that in the U.S. up to 500,000 birds die annually due to collisions with wind turbines. This article proposes a system for mitigating bird mortality around wind farms. The solution is based on a stereo-vision system embedded in distributed computing and IoT paradigms. After a bird’s detection in a defined zone, the decision-making system activates a collision avoidance routine composed of light and sound deterrents and the turbine stopping procedure. The development process applies a User-Driven Design approach along with the process of component selection and heuristic adjustment. This proposal includes a bird detection method and localization procedure. The bird identification is carried out using artificial intelligence algorithms. Validation tests with a fixed-wing drone and verifying observations by ornithologists proved the system’s desired reliability of detecting a bird with wingspan over 1.5 m from at least 300 m. Moreover, the suitability of the system to classify the size of the detected bird into one of three wingspan categories, small, medium and large, was confirmed. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

Place, publisher, year, edition, pages
MDPI AG, 2021
Keywords
Artificial intelligence, Bird monitoring system, Distributed computing, Environmental sustainability, Monitoring of avifauna, Safety system, Stereo-vision, Vision system, Aircraft detection, Decision making, Electric utilities, Fixed wings, Stereo image processing, Stereo vision, Wind power, Wind turbines, Artificial intelligence algorithms, Component selection, Decision-making systems, Design approaches, Development process, Localization procedure, Renewable energy source, Stereo vision system, Birds, algorithm, article, avoidance behavior, bird, human, mortality, nonhuman, reliability, renewable energy, sound, vision, wind farm, wing
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:bth-20993 (URN)10.3390/s21010267 (DOI)000606059700001 ()33401575 (PubMedID)2-s2.0-85099421498 (Scopus ID)
Note

open access

Available from: 2021-02-01 Created: 2021-02-01 Last updated: 2022-02-10Bibliographically approved

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