Sensors and Algorithms in Industry 4.0 : Security and Health Preservation Applications

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

Comprehensive Bird Preservation at Wind Farms

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

Sensors and Algorithms in Industry 4.0 : Security and Health Preservation Applications

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

A system for heart sounds classification

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.open access</p

Application of Radar Solutions for the Purpose of Bird Tracking Systems Based on Video Observation

Wildlife Hazard Management is nowadays a very serious problem, mostly at airports and wind farms. If ignored, it may lead to repercussions in human safety, ecology, and economics. One of the approaches that is widely implemented in small and medium-size airports, as well as on wind turbines is based on a stereo-vision. However, to provide long-term observations allowing the determination of the hot spots of birds’ activity and forecast future events, a robust tracking algorithm is required. The aim of this paper is to review tracking algorithms widely used in Radar Science and assess the possibilities of application of these algorithms for the purpose of tracking birds with a stereo-vision system. We performed a survey-of-related works and simulations determined five state-of-the art algorithms: Kalman Filter, Nearest-Neighbour, Joint-Probabilistic Data Association, and Interacting Multiple Model with the potential for implementation in a stereo-vision system. These algorithms have been implemented and simulated in the proposed case stud

A flowchart of the hybrid LPC-SVM-MCS system training process.

<p>The system first collects PCG signals and performs their segmentation to extract useful information for LPC estimation. Then the training process commences where the Modified Cuckoo Search algorithm optimizes parameters of a Support Vector Machine classifier.</p

Spectrum comparison of selected heart sounds and LPC filters.

<p>Presented curves demonstrate the effectiveness of the modified LPC algorithm in estimating different heart sounds.</p

Example heart sounds used in the tests.

<p>A – early systolic murmur, B – S4, C – pansystolic murmur, D – S3, E – late systolic murmur, F – normal split S2, G – normal split S1, H – ejection click, I – diastolic rumble, J – opening snap.</p

A Wavelet Transform-Based Neural Network Denoising Algorithm for Mobile Phonocardiography

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.open access</p