An in situ probe for on-line monitoring of cell density and viability on the basis of dark field microscopy in conjunction with image processing and supervised machine learning

2007 ◽  
Vol 97 (6) ◽  
pp. 1489-1500 ◽  
Author(s):  
Ning Wei ◽  
Jia You ◽  
Karl Friehs ◽  
Erwin Flaschel ◽  
Tim Wilhelm Nattkemper
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Cell Density ◽  
Dark Field ◽  
Supervised Machine Learning ◽  
Dark Field Microscopy ◽  
On Line

In situ dark field microscopy for on-line monitoring of yeast cultures

2006 ◽  
Vol 29 (3) ◽  
pp. 373-378 ◽  
Author(s):  
Ning Wei ◽  
Jia You ◽  
Karl Friehs ◽  
Erwin Flaschel ◽  
Tim Wilhelm Nattkemper
Keyword(s):  
Dark Field ◽  
Yeast Cultures ◽  
Dark Field Microscopy ◽  
On Line

scholarly journals Identification of abnormal tribological regimes using a microphone and semi-supervised machine-learning algorithm

Friction ◽  
2021 ◽  
Author(s):  
Vigneashwara Pandiyan ◽  
Josef Prost ◽  
Georg Vorlaufer ◽  
Markus Varga ◽  
Kilian Wasmer
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Industrial Applications ◽  
Supervised Machine Learning ◽  
Normal Operation ◽  
Promising Alternative ◽  
Wear And Tear ◽  
Validation Procedure ◽  
And Performance

AbstractFunctional surfaces in relative contact and motion are prone to wear and tear, resulting in loss of efficiency and performance of the workpieces/machines. Wear occurs in the form of adhesion, abrasion, scuffing, galling, and scoring between contacts. However, the rate of the wear phenomenon depends primarily on the physical properties and the surrounding environment. Monitoring the integrity of surfaces by offline inspections leads to significant wasted machine time. A potential alternate option to offline inspection currently practiced in industries is the analysis of sensors signatures capable of capturing the wear state and correlating it with the wear phenomenon, followed by in situ classification using a state-of-the-art machine learning (ML) algorithm. Though this technique is better than offline inspection, it possesses inherent disadvantages for training the ML models. Ideally, supervised training of ML models requires the datasets considered for the classification to be of equal weightage to avoid biasing. The collection of such a dataset is very cumbersome and expensive in practice, as in real industrial applications, the malfunction period is minimal compared to normal operation. Furthermore, classification models would not classify new wear phenomena from the normal regime if they are unfamiliar. As a promising alternative, in this work, we propose a methodology able to differentiate the abnormal regimes, i.e., wear phenomenon regimes, from the normal regime. This is carried out by familiarizing the ML algorithms only with the distribution of the acoustic emission (AE) signals captured using a microphone related to the normal regime. As a result, the ML algorithms would be able to detect whether some overlaps exist with the learnt distributions when a new, unseen signal arrives. To achieve this goal, a generative convolutional neural network (CNN) architecture based on variational auto encoder (VAE) is built and trained. During the validation procedure of the proposed CNN architectures, we were capable of identifying acoustics signals corresponding to the normal and abnormal wear regime with an accuracy of 97% and 80%. Hence, our approach shows very promising results for in situ and real-time condition monitoring or even wear prediction in tribological applications.

scholarly journals Augmenting biologging with supervised machine learning to studyin situbehavior of the medusaChrysaora fuscescens

2019 ◽  
Author(s):  
Clara Fannjiang ◽  
T. Aran Mooney ◽  
Seth Cones ◽  
David Mann ◽  
K. Alex Shorter ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Laboratory Data ◽  
Supervised Machine Learning ◽  
Motion Sensors ◽  
Fine Scale ◽  
Video Footage ◽  
Research Findings ◽  
Summary Statement

AbstractZooplankton occupy critical roles in marine ecosystems, yet their fine-scale behavior remains poorly understood due to the difficulty of studying individualsin situ. Here we combine biologging with supervised machine learning (ML) to demonstrate a pipeline for studyingin situbehavior of larger zooplankton such as jellyfish. We deployed the ITAG, a biologging package with high-resolution motion sensors designed for soft-bodied invertebrates, on 8Chrysaora fuscescensin Monterey Bay, using the tether method for retrieval. Using simultaneous video footage of the tagged jellyfish, we develop ML methods to 1) identify periods of tag data corrupted by the tether method, which may have compromised prior research findings, and 2) classify jellyfish behaviors. Our tools yield characterizations of fine-scale jellyfish activity and orientation over long durations, and provide evidence that developing behavioral classifiers onin siturather than laboratory data is essential.Summary StatementHigh-resolution motion sensors paired with supervised machine learning can be used to infer fine-scalein situbehavior of zooplankton for long durations.

scholarly journals The NoisyOffice Database: A Corpus To Train Supervised Machine Learning Filters For Image Processing

2019 ◽  
Vol 63 (11) ◽  
pp. 1658-1667
Author(s):  
M J Castro-Bleda ◽  
S España-Boquera ◽  
J Pastor-Pellicer ◽  
F Zamora-Martínez
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Deep Learning ◽  
Supervised Learning ◽  
Image Enhancement ◽  
Super Resolution ◽  
Supervised Machine Learning ◽  
Text Documents ◽  
Learning Techniques ◽  
Printed Text

Abstract This paper presents the ‘NoisyOffice’ database. It consists of images of printed text documents with noise mainly caused by uncleanliness from a generic office, such as coffee stains and footprints on documents or folded and wrinkled sheets with degraded printed text. This corpus is intended to train and evaluate supervised learning methods for cleaning, binarization and enhancement of noisy images of grayscale text documents. As an example, several experiments of image enhancement and binarization are presented by using deep learning techniques. Also, double-resolution images are also provided for testing super-resolution methods. The corpus is freely available at UCI Machine Learning Repository. Finally, a challenge organized by Kaggle Inc. to denoise images, using the database, is described in order to show its suitability for benchmarking of image processing systems.

scholarly journals Molecular Epidemiology of Oral Treponemes Associated with Periodontal Disease

1998 ◽  
Vol 36 (5) ◽  
pp. 1399-1403 ◽  
Author(s):  
Annette Moter ◽  
Carina Hoenig ◽  
Bong-Kyu Choi ◽  
Birgit Riep ◽  
Ulf B. Göbel
Keyword(s):  
In Situ Hybridization ◽  
Alveolar Bone ◽  
Blot Hybridization ◽  
Dark Field ◽  
Causative Role ◽  
Oligonucleotide Probes ◽  
Dot Blot ◽  
Dark Field Microscopy ◽  
Healthy Control

Periodontitis, a disease responsible for tooth loss worldwide, is characterized by chronic inflammation of the periodontium, eventually leading to destruction of periodontal ligaments and supporting alveolar bone. Spirochetes, identified by dark-field microscopy as being the most predominant bacteria in advanced lesions, are thought to play a causative role. Various spirochetal morphotypes were observed, but most of these morphotypes are as yet uncultivable. To assess the role of these organisms we designed oligonucleotide probes for the identification of both cultivable and so far uncultivable spirochetes in periodontitis patients. Subgingival plaque specimens taken from diseased sites (n = 200) and healthy control sites (n = 44) from 53 patients with rapidly progressive periodontitis (RPP) were submitted to direct in situ hybridization or dot blot hybridization after prior amplification with eubacterial primers. Spirochetes were found in all patients, but their distributions varied considerably. Parallel use of oligonucleotide probes specific for cultivable or so far uncultivable treponemes suggested the presence of novel yet unknown organisms at a high frequency. These uncultivable treponemes were visualized by fluorescence in situ hybridization, and their morphologies, sizes, and numbers could be estimated. All RPP patients included in this study harbored oral treponemes that represent either novel species, e.g.,Treponema maltophilum, or uncultivable phylotypes. Therefore, it is necessary to include these organisms in etiologic considerations and to strengthen efforts to cultivate these as yet uncultivable treponemes.

scholarly journals Influence of GUJarati STEmmeR in Supervised Learning of Web Page Categorization

2021 ◽  
Vol 13 (3) ◽  
pp. 23-34
Author(s):  
Chandrakant D. Patel ◽  
◽  
Jayesh M. Patel
Keyword(s):  
Machine Learning ◽  
Information Retrieval ◽  
Research Work ◽  
Research Problem ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Web Page ◽  
User Query ◽  
On Line ◽  
Gujarati Language

With the large quantity of information offered on-line, it's equally essential to retrieve correct information for a user query. A large amount of data is available in digital form in multiple languages. The various approaches want to increase the effectiveness of on-line information retrieval but the standard approach tries to retrieve information for a user query is to go looking at the documents within the corpus as a word by word for the given query. This approach is incredibly time intensive and it's going to miss several connected documents that are equally important. So, to avoid these issues, stemming has been extensively utilized in numerous Information Retrieval Systems (IRS) to extend the retrieval accuracy of all languages. These papers go through the problem of stemming with Web Page Categorization on Gujarati language which basically derived the stem words using GUJSTER algorithms [1]. The GUJSTER algorithm is based on morphological rules which is used to derived root or stem word from inflected words of the same class. In particular, we consider the influence of extracted a stem or root word, to check the integrity of the web page classification using supervised machine learning algorithms. This research work is intended to focus on the analysis of Web Page Categorization (WPC) of Gujarati language and concentrate on a research problem to do verify the influence of a stemming algorithm in a WPC application for the Gujarati language with improved accuracy between from 63% to 98% through Machine Learning supervised models with standard ratio 80% as training and 20% as testing.

Clear visualization of the products of nonradioactive in situ hybridization in plant tissue by simple dark-field microscopy

Micron ◽  
1997 ◽  
Vol 28 (3) ◽  
pp. 185-187 ◽  
Author(s):  
Sachihiro Matsunaga ◽  
Shigeyuki Kawano ◽  
Tetsuya Higashiyama ◽  
Noriko Inada ◽  
Tsuneyoshi Kuroiwa
Keyword(s):  
In Situ Hybridization ◽  
Plant Tissue ◽  
Dark Field ◽  
Dark Field Microscopy ◽  
Nonradioactive In Situ Hybridization

scholarly journals In-situ identification of material batches using machine learning for machining operations

2020 ◽  
Author(s):  
Benjamin Lutz ◽  
Dominik Kisskalt ◽  
Andreas Mayr ◽  
Daniel Regulin ◽  
Matteo Pantano ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Accuracy ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Machine Learning Model ◽  
Multiple Classification ◽  
Subtractive Manufacturing ◽  
Smart Service ◽  
Machining Operations

AbstractIn subtractive manufacturing, differences in machinability among batches of the same material can be observed. Ignoring these deviations can potentially reduce product quality and increase manufacturing costs. To consider the influence of the material batch in process optimization models, the batch needs to be efficiently identified. Thus, a smart service is proposed for in-situ material batch identification. This service is driven by a supervised machine learning model, which analyzes the signals of the machine’s control, especially torque data, for batch classification. The proposed approach is validated by cutting experiments with five different batches of the same specified material at various cutting conditions. Using this data, multiple classification models are trained and optimized. It is shown that the investigated batches can be correctly identified with close to 90% prediction accuracy using machine learning. Out of all the investigated algorithms, the best results are achieved using a Support Vector Machine with 89.0% prediction accuracy for individual batches and 98.9% while combining batches of similar machinability.

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