Semi-supervised detection of industrial fouling using ultrasound

Fouling is a large scale problem in industrial equipment such as heat exchangers or pipes, used in factories, ships, airplanes, etc. Traditionally, such equipment is cleaned using sandblasting, chemicals or mechanical methods, all of which require halting the process, which is costly. Recently, high-power ultrasound has become a viable option to these methods. In ultrasonic cleaning ultrasound is projected into the equipment from the outside, which means that the equipment does not need to be halted to perform cleaning. While the cleaning itself is not invasive in nature, in most cases vision cannot be used to determine whether cleaning is actually necessary or not. What remains is to have such a method that is also non-invasive. It is possible to use ultrasound as a kind of a radar to detect whether or not fouling is present, and this has been attempted in previous literature. However, until now, such methods have required extensive manual calculation and knowledge of the physical properties of the setup. We present the first ever system to concurrently clean and detect industrial fouling using ultrasound and deep learning. Our method does not rely on specific properties of the equipment, allowing it to generalize to large industrial processes where it is not practical to calculate or simulate the cleaning scenario. To this end, we extend existing literature on semi-supervised learning by presenting algorithms used to learn from a monotonic process, and model the high-dimensional signal data using a convolutional neural network that is highly robust to temporal variance. This thesis presents the machine learning solution behind the system, and the cleaning components are provided by Altum Technologies. Further, we explore methods to detect and counter the so-called domain shift that occurs when experimenting in the physical world, and provide experimental evidence that our methods work in practice

Snapshot hyperspectral imaging using wide dilation networks

Hyperspectral (HS) cameras record the spectrum at multiple wavelengths for each pixel in an image, and are used, e.g., for quality control and agricultural remote sensing. We introduce a fast, cost-efficient and mobile method of taking HS images using a regular digital camera equipped with a passive diffraction grating filter, using machine learning for constructing the HS image. The grating distorts the image by effectively mapping the spectral information into spatial dislocations, which we convert into a HS image by a convolutional neural network utilizing novel wide dilation convolutions that accurately model optical properties of diffraction. We demonstrate high-quality HS reconstruction using a model trained on only 271 pairs of diffraction grating and ground truth HS images.Peer reviewe

Lääketutka - Löytöretkeilyä ihmisten keskusteluihin omasta terveydestään

Peer reviewe

Adenylate Kinase 3 Sensitizes Cells to Cigarette Smoke Condensate Vapor Induced Cisplatin Resistance

Background: The major established etiologic risk factor for bladder cancer is cigarette smoking and one of the major antineoplastic agents used for the treatment of advanced bladder cancer is cisplatin. A number of reports have suggested that cancer patients who smoke while receiving treatment have lower rates of response and decreased efficacy of cancer therapies. Methodology/Principal Findings: In this study, we investigated the effect of cigarette smoke condensate (CSC) vapor on cisplatin toxicity in urothelial cell lines SV-HUC-1 and SCaBER cells. We showed that chronic exposure to CSC vapor induced cisplatin resistance in both cell lines. In addition, we found that the expression of mitochondrial-resident protein adenylate kinase-3 (AK3) is decreased by CSC vapor. We further observed that chronic CSC vapor-exposed cells displayed decreased cellular sensitivity to cisplatin, decreased mitochondrial membrane potential (DYm) and increased basal cellular ROS levels compared to unexposed cells. Re-expression of AK3 in CSC vapor-exposed cells restored cellular sensitivity to cisplatin. Finally, CSC vapor increased the growth of the tumors and also curtail the response of tumor cells to cisplatin chemotherapy in vivo. Conclusions/Significance: The current study provides evidence that chronic CSC vapor exposure affects AK3 expression an

Detecting Industrial Fouling by Monotonicity during Ultrasonic Cleaning

High power ultrasound permits non-invasive cleaning of industrial equipment, but to make such cleaning systems energy efficient, one needs to recognize when the structure has been sufficiently cleaned without using invasive diagnostic tools. This can be done using ultrasound reflections generated inside the structure. This inverse modeling problem cannot be solved by forward modeling for irregular and complex structures, and it is difficult to tackle also with machine learning since human-annotated labels are hard get. We provide a deep learning solution that relies on the physical properties of the cleaning process. We rely on the fact that the amount of fouling is reduced as we clean more. Using this monotonicity property as indirect supervision we develop a semi-supervised model for detecting when the equipment has been cleaned.Peer reviewe

Ultrasonic Fouling Detector Powered by Machine Learning

Guided waves can be used to monitor structural health in industrial pipelines, and e.g. allow detection of accumulated precipitation on the surface of pipe. Propagation of guided waves in a tubular structure carrying possible fouling can be separated from a clean structure due to variation in wave propagation properties at the fouled area. In addition, multiple propagation paths around the tubular structure allow locating the fouled areas. In this study, we obtained dispersion curves of a tubular structure loaded with a local fouling layer of different thickness by using numerical simulations. We combined the dispersion curve information with simulated and measured times-of-arrival of guided wave propagation to second order helicoidal paths and used a Gaussian Process machine learning approach to estimate location of fouling on a steel pipe.Peer reviewe

In silico comparative genomics analysis of Plasmodium falciparum for the identification of putative essential genes and therapeutic candidates.

A sequence of computational methods was used for predicting novel drug targets against drug resistant malaria parasite Plasmodium falciparum. Comparative genomics, orthologous protein analysis among same and other malaria parasites and protein-protein interaction study provide us new insights into determining the essential genes and novel therapeutic candidates. Among the predicted list of 21 essential proteins from unique pathways, 11 proteins were prioritized as anti-malarial drug targets. As a case study, we built homology models of two uncharacterized proteins using MODELLER v9.13 software from possible templates. Functional annotation of these proteins was done by the InterPro databases and from ProBiS server by comparison of predicted binding site residues. The model has been subjected to in silico docking study with screened potent lead compounds from the ZINC database by Dock Blaster software using AutoDock 4. Results from this study facilitate the selection of proteins and putative inhibitors for entry into drug design production pipelines

MicroRNA-31 is required for astrocyte specification

Previously, we determined microRNA-31 (miR-31) is a noncoding tumor suppressive gene frequently deleted in glioblastoma (GBM); miR-31 suppresses tumor growth, in part, by limiting the activity of NF-κB. Herein, we expand our previous studies by characterizing the role of miR-31 during neural precursor cell (NPC) to astrocyte differentiation. We demonstrate that miR-31 expression and activity is suppressed in NPCs by stem cell factors such as Lin28, c-Myc, SOX2 and Oct4. However, during astrocytogenesis, miR-31 is induced by STAT3 and SMAD1/5/8, which mediate astrocyte differentiation. We determined miR-31 is required for terminal astrocyte differentiation, and that the loss of miR-31 impairs this process and/or prevents astrocyte maturation. We demonstrate that miR-31 promotes astrocyte development, in part, by reducing the levels of Lin28, a stem cell factor implicated in NPC renewal. These data suggest that miR-31 deletions may disrupt astrocyte development and/or homeostasis

Toward on-sky adaptive optics control using reinforcement learning Model-based policy optimization for adaptive optics

Context. The direct imaging of potentially habitable exoplanets is one prime science case for the next generation of high contrast imaging instruments on ground-based, extremely large telescopes. To reach this demanding science goal, the instruments are equipped with eXtreme Adaptive Optics (XAO) systems which will control thousands of actuators at a framerate of kilohertz to several kilohertz. Most of the habitable exoplanets are located at small angular separations from their host stars, where the current control laws of XAO systems leave strong residuals. Aims. Current AO control strategies such as static matrix-based wavefront reconstruction and integrator control suffer from a temporal delay error and are sensitive to mis-registration, that is, to dynamic variations of the control system geometry. We aim to produce control methods that cope with these limitations, provide a significantly improved AO correction, and, therefore, reduce the residual flux in the coronagraphic point spread function (PSF). Methods. We extend previous work in reinforcement learning for AO. The improved method, called the Policy Optimization for Adaptive Optics (PO4AO), learns a dynamics model and optimizes a control neural network, called a policy. We introduce the method and study it through numerical simulations of XAO with Pyramid wavefront sensor (PWFS) for the 8-m and 40-m telescope aperture cases. We further implemented PO4AO and carried out experiments in a laboratory environment using Magellan Adaptive Optics eXtreme system (MagAO-X) at the Steward laboratory. Results. PO4AO provides the desired performance by improving the coronagraphic contrast in numerical simulations by factors of 3-5 within the control region of deformable mirror and PWFS, both in simulation and in the laboratory. The presented method is also quick to train, that is, on timescales of typically 5-10 s, and the inference time is sufficiently small (Peer reviewe

Toward on-sky adaptive optics control using reinforcement learning Model-based policy optimization for adaptive optics

Context. The direct imaging of potentially habitable exoplanets is one prime science case for the next generation of high contrast imaging instruments on ground-based, extremely large telescopes. To reach this demanding science goal, the instruments are equipped with eXtreme Adaptive Optics (XAO) systems which will control thousands of actuators at a framerate of kilohertz to several kilohertz. Most of the habitable exoplanets are located at small angular separations from their host stars, where the current control laws of XAO systems leave strong residuals. Aims. Current AO control strategies such as static matrix-based wavefront reconstruction and integrator control suffer from a temporal delay error and are sensitive to mis-registration, that is, to dynamic variations of the control system geometry. We aim to produce control methods that cope with these limitations, provide a significantly improved AO correction, and, therefore, reduce the residual flux in the coronagraphic point spread function (PSF). Methods. We extend previous work in reinforcement learning for AO. The improved method, called the Policy Optimization for Adaptive Optics (PO4AO), learns a dynamics model and optimizes a control neural network, called a policy. We introduce the method and study it through numerical simulations of XAO with Pyramid wavefront sensor (PWFS) for the 8-m and 40-m telescope aperture cases. We further implemented PO4AO and carried out experiments in a laboratory environment using Magellan Adaptive Optics eXtreme system (MagAO-X) at the Steward laboratory. Results. PO4AO provides the desired performance by improving the coronagraphic contrast in numerical simulations by factors of 3-5 within the control region of deformable mirror and PWFS, both in simulation and in the laboratory. The presented method is also quick to train, that is, on timescales of typically 5-10 s, and the inference time is sufficiently small (Peer reviewe