Application of on-line adaptable Neural Network for the rolling force set-up of a plate mill

2004 ◽  
Vol 17 (5) ◽  
pp. 557-565 ◽  
Author(s):  
Duk Man Lee ◽  
S.G Choi
Keyword(s):  
Neural Network ◽  
Rolling Force ◽  
Plate Mill ◽  
On Line ◽  
Set Up

scholarly journals MEYE: Web-app for translational and real-time pupillometry

2021 ◽  
Author(s):  
Raffaele Mazziotti ◽  
Fabio Carrara ◽  
Aurelia Viglione ◽  
Leonardo Lupori ◽  
Luca Lo Verde ◽  
...  
Keyword(s):  
Neural Network ◽  
Mouse Models ◽  
State Of The Art ◽  
Web Browser ◽  
Non Invasive ◽  
Temporal Specificity ◽  
On Line ◽  
Web App ◽  
Set Up ◽  
Neuropsychiatric Conditions

AbstractPupil dynamics alterations have been found in patients affected by a variety of neuropsychiatric conditions, including autism. Studies in mouse models have used pupillometry for phenotypic assessment and as a proxy for arousal. Both in mice and humans, pupillometry is non-invasive and allows for longitudinal experiments supporting temporal specificity, however its measure requires dedicated setups. Here, we introduce a Convolutional Neural Network that performs on-line pupillometry in both mice and humans in a web app format. This solution dramatically simplifies the usage of the tool for non-specialist and non-technical operators. Because a modern web browser is the only software requirement, this choice is of great interest given its easy deployment and set-up time reduction. The tested model performances indicate that the tool is sensitive enough to detect both spontaneous and evoked pupillary changes, and its output is comparable with state-of-the-art commercial devices.

A study on on-line learning neural network for prediction for rolling force in hot-rolling mill

2005 ◽  
Vol 164-165 ◽  
pp. 1612-1617 ◽  
Author(s):  
Joon-Sik Son ◽  
Duk-Man Lee ◽  
Ill-Soo Kim ◽  
Seung-Gap Choi
Keyword(s):  
Neural Network ◽  
Hot Rolling ◽  
Rolling Mill ◽  
Rolling Force ◽  
On Line ◽  
Hot Rolling Mill ◽  
On Line Learning

Prediction of Thermally Induced Time-Variant Machine Tool Error Maps Using a Fuzzy Artmap Neural Network

1997 ◽  
Vol 119 (4A) ◽  
pp. 623-630 ◽  
Author(s):  
N. Srinivasa ◽  
J. C. Ziegert
Keyword(s):  
Neural Network ◽  
Machine Tool ◽  
Duty Cycle ◽  
Machine Tools ◽  
Cutting Tool ◽  
Continuous Process ◽  
Accurate Method ◽  
Fuzzy Artmap ◽  
On Line ◽  
Set Up

A novel approach to on-line learning and prediction of time-variant machine tool error maps is proposed. These error maps are measured using a fast calibration device called the laser ball-bar (LBB) that directly measures the total positioning errors at the cutting tool using trilateration. The learning and prediction of these error maps is achieved using a Fuzzy ARTMAP neural network by treating the problem as an incremental approximation of a functional mapping between thermal sensor readings and the associated positional errors at each location of the cutting tool. Experimental measurements of the positional errors for a two axis turning center were performed using the LBB over two separate thermal duty cycles. The Fuzzy ARTMAP was trained on-line using the data collected over the first thermal duty cycle, which simulated machining of large workpieces with several hours of machining, inspection and set-up time. The network was made to predict the error map of the machine for a new thermal duty cycle that simulated machining of a range of short and long workpieces with shorter machining and set-up times. Results of these predictions show that the LBB and Fuzzy ARTMAP combination is a fast and accurate method for real-time error compensation in machine tools. This method overcomes drawbacks in currently methodologies including high cost and excessive downtime to calibrate machine tools. Application of the Fuzzy ARTMAP to continuous process improvement is discussed.

Detection and localization of helipad in autonomous UAV landing: a coupled visual-inertial approach with artificial intelligence

2020 ◽  
Vol 71 (7) ◽  
pp. 828-839
Author(s):  
Thinh Hoang Dinh ◽  
Hieu Le Thi Hong
Keyword(s):  
Neural Network ◽  
Unmanned Aerial Vehicles ◽  
Fiducial Marker ◽  
Localization Algorithm ◽  
Challenging Problem ◽  
Autonomous Landing ◽  
Aerial Vehicles ◽  
Set Up ◽  
Detection And Localization ◽  
Rotary Wing

Autonomous landing of rotary wing type unmanned aerial vehicles is a challenging problem and key to autonomous aerial fleet operation. We propose a method for localizing the UAV around the helipad, that is to estimate the relative position of the helipad with respect to the UAV. This data is highly desirable to design controllers that have robust and consistent control characteristics and can find applications in search – rescue operations. AI-based neural network is set up for helipad detection, followed by optimization by the localization algorithm. The performance of this approach is compared against fiducial marker approach, demonstrating good consensus between two estimations

Respirometry for determination of the influents SS-concentration

1996 ◽  
Vol 33 (1) ◽  
pp. 311-323 ◽  
Author(s):  
A. Witteborg ◽  
A. van der Last ◽  
R. Hamming ◽  
I. Hemmers
Keyword(s):  
Experimental Design ◽  
Activated Sludge ◽  
Substrate Concentration ◽  
Full Scale ◽  
Activated Sludge Process ◽  
Respiration Rates ◽  
On Line ◽  
Large Measurement ◽  
Set Up

A method is presented for determining influent readily biodegradable substrate concentration (SS). The method is based on three different respiration rates, which can be measured with a continuous respiration meter which is operated in a cyclic way. Within the respiration meter nitrification is inhibited through the addition of ATU. Simulations were used to develop the respirometry set-up and decide upon the experimental design. The method was tested as part of a large measurement programme executed at a full-scale plant. The proposed respirometry set-up has been shown to be suitable for a semi-on-line determination of an influent SS which is fully based on the IAWQ #1 vision of the activated sludge process. The YH and the KS play a major role in the principle, and should be measured directly from the process.

scholarly journals Active Learning with Bayesian UNet for Efficient Semantic Image Segmentation

2021 ◽  
Vol 7 (2) ◽  
pp. 37
Author(s):  
Isah Charles Saidu ◽  
Lehel Csató
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Active Learning ◽  
Convolutional Neural Network ◽  
Medical Image ◽  
Segmentation Method ◽  
Semantic Image Segmentation ◽  
Batch Normalization ◽  
Set Up ◽  
Image Datasets

We present a sample-efficient image segmentation method using active learning, we call it Active Bayesian UNet, or AB-UNet. This is a convolutional neural network using batch normalization and max-pool dropout. The Bayesian setup is achieved by exploiting the probabilistic extension of the dropout mechanism, leading to the possibility to use the uncertainty inherently present in the system. We set up our experiments on various medical image datasets and highlight that with a smaller annotation effort our AB-UNet leads to stable training and better generalization. Added to this, we can efficiently choose from an unlabelled dataset.

WWW.TELD.NET: Online Courseware Engine for Teaching by Examples and Learning by Doing

2001 ◽  
Vol 29 (3) ◽  
pp. 219-235 ◽  
Author(s):  
G. Q. Huang ◽  
B. Shen ◽  
K. L. Mak
Keyword(s):  
Teaching And Learning ◽  
Virtual Classroom ◽  
Learning By Doing ◽  
General Purpose ◽  
Project Based Learning ◽  
Course Materials ◽  
Summative Assessments ◽  
On Line ◽  
Set Up ◽  
Electronic Delivery

TELD stands for “Teaching by Examples and Learning by Doing.” It is an on-line courseware engine over the World Wide Web. There are four folds of meanings in TELD. First, TELD represents a teaching and learning method that unifies a number of contemporary methods such as Problem-Based Learning (PBL) in medical education, Project-Based Learning (PBL) in engineering education, and Case Method (CM) in business education. Second, TELD serves as a Web server for hosting teaching and learning materials especially based on the TELD method. A variety of on-line facilities are provided for editing and uploading course materials such as syllabus, schedule, curriculum, examples of case study, exercises of mini-project, formative and summative assessments, etc. Third, TELD is a courseware search engine where educators are able to register their course materials and search for materials suitable for a particular course. In contrast with general-purpose search engines, TELD is set up for the special purpose of education. Therefore, the time and efforts spent on surfing are expected to be reduced dramatically. Finally, TELD is an on-line virtual classroom for electronic delivery of electronic curriculum materials. In addition to providing the lecture notes, TELD not only provides discussion questions for conducting in-class discussions and homework as formative assessment but also provides facilities for students to plan and submit their group work. This article presents an overview of the TELD courseware engine together with its background and underlying philosophy.

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