Investigating Machine Learning and Control Theory Approaches for Process Fault Detection: A Comparative Study of KPCA and the Observer-Based Method.

The paper focuses on the importance of prompt and efficient process fault detection in contemporary manufacturing industries, where product quality and safety protocols are critical. The study compares the efficiencies of two techniques for process fault detection: Kernel Principal Component Analysis (KPCA) and the observer method. Both techniques are applied to observe water volume variation within a hydraulic system comprising three tanks. PCA is an unsupervised learning technique used for dimensionality reduction and pattern recognition. It is an extension of Principal Component Analysis (PCA) that utilizes kernel functions to transform data into higher-dimensional spaces, where it becomes easier to separate classes or identify patterns. In this paper, KPCA is applied to detect faults in the hydraulic system by analyzing the variation in water volume. The observer method originates from control theory and is utilized to estimate the internal states of a system based on its output measurements. It is commonly used in control systems to estimate the unmeasurable or hidden states of a system, which is crucial for ensuring proper control and fault detection. In this study, the observer method is applied to the hydraulic system to estimate the water volume variations within the three tanks. The paper presents a comparative study of these two techniques applied to the hydraulic system. The results show that both KPCA and the observer method perform similarly in detecting faults within the system. This similarity in performance highlights the efficacy of these techniques and their potential adaptability in various fault diagnosis scenarios within modern manufacturing processes.

Artificial Intelligence for Cardiac Diseases Diagnosis and Prediction Using ECG Images on Embedded Systems.

The electrocardiogram (ECG) provides essential information about various human cardiac conditions. Several studies have investigated this topic in order to detect cardiac abnormalities for prevention purposes. Nowadays, there is an expansion of new smart signal processing methods, such as machine learning and its sub-branches, such as deep learning. These popular techniques help analyze and classify the ECG signal in an efficient way. Our study aims to develop algorithmic models to analyze ECG tracings to predict cardiovascular diseases. The direct impact of this work is to save lives and improve medical care with less expense. As health care and health insurance costs increase in the world, the direct impact of this work is saving lives and improving medical care. We conducted numerous experiments to optimize deep-learning parameters. We found the same validation accuracy value of about 0.95 for both MobileNetV2 and VGG16 algorithms. After implementation on Raspberry Pi, our results showed a small decrease in accuracy (0.94 and 0.90 for MobileNetV2 and VGG16 algorithms, respectively). Therefore, the main purpose of the present research work is to improve, in an easy and cheaper way, real-time monitoring using smart mobile tools (mobile phones, smart watches, connected T-shirts, etc.).

Effect of a static magnetic field on Escherichia coli adhesion and orientation.

This preliminary study focused on the effect of exposure to 0.5 T static magnetic fields on Escherichia coli adhesion and orientation. We investigated the difference in bacterial adhesion on the surface of glass and indium tin oxide-coated glass when exposed to a magnetic field either perpendicular or parallel to the adhesion surface (vectors of magnetic induction are perpendicular or parallel to the adhesion surface, respectively). Control cultures were simultaneously grown under identical conditions but without exposure to the magnetic field. We observed a decrease in cell adhesion after exposure to the magnetic field. Orientation of bacteria cells was affected after exposure to a parallel magnetic field. On the other hand, no effect on the orientation of bacteria cells was observed after exposure to a perpendicular magnetic field.

AFM, CLSM and EIS characterization of the immobilization of antibodies on indium-tin oxide electrode and their capture of Legionella pneumophila.

The microscopic surface molecular structures and properties of monoclonal anti-Legionella pneumophila antibodies on an indium-tin oxide (ITO) electrode surface were studied to elaborate an electrochemical immunosensor for Legionella pneumophila detection. A monoclonal anti-Legionella pneumophila antibody (MAb) has been immobilized onto an ITO electrode via covalent chemical bonds between antibodies amino-group and the ring of (3-Glycidoxypropyl) trimethoxysilane (GPTMS). The functionalization of the immunosensor was characterized by atomic force microscopy (AFM), water contact angle measurement, cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS) in the presence of [Fe(CN)6](3-/4-) as a redox probe. Specific binding of Legionella pneumophila sgp 1 cells onto the antibody-modified ITO electrode was shown by confocal laser scanning microscopy (CLSM) imaging and EIS. AFM images evidenced the dense and relatively homogeneous morphology on the ITO surface. The formation of the complex epoxysilane-antibodies acting as barriers for the electron transfer between the electrode surface and the redox species in the solution induced a significant increase in the charge transfer resistance (Rct) compared to all the electric elements. A linear relationship between the change in charge transfer resistance (ΔRct=Rct after immunoreactions - Rct control) and the logarithmic concentration value of L. pneumophila was observed in the range of 5 × 10(1)-5 × 10(4) CFU mL(-1) with a limit of detection 5 × 10(1)CFU mL(-1). The present study has demonstrated the successful deposition of an anti-L. pneumophila antibodies on an indium-tin oxide surface, opening its subsequent use as immuno-captor for the specific detection of L. pneumophila in environmental samples.