scholarly journals A Noncontact Method for the Detection and Diagnosis of Surface Damage in Immersed Structures

2015 ◽  
Vol 2015 ◽  
pp. 1-10
Author(s):  
Y. Sidibe ◽  
F. Druaux ◽  
D. Lefebvre ◽  
F. Leon ◽  
G. Maze
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Laboratory Experiments ◽  
Learning Algorithm ◽  
Surface Damage ◽  
Economic Losses ◽  
Diagnosis Method ◽  
Detection And Diagnosis ◽  
Specific Learning ◽  
Ultrasonic Echography

Detection and diagnosis method is proposed for surface damage in immersed structures. It is based on noncontact ultrasonic echography measurements, signal processing tools, and artificial intelligence methods. Significant features are extracted from the measured signals and a classification method is developed to detect the echoes resulting from surface damage in an immersed structure. The identification of the damage is also provided. Gaussian neural networks trained with a specific learning algorithm are developed for this purpose. The performance of the method is validated by laboratory experiments which indicate that this method could be suitable for the monitoring of inaccessible systems like marine turbines whose unavailability causes severe economic losses.

scholarly journals Big data, machine learning and artificial intelligence: a neurologist’s guide

2020 ◽  
pp. practneurol-2020-002688
Author(s):  
Stephen D Auger ◽  
Benjamin M Jacobs ◽  
Ruth Dobson ◽  
Charles R Marshall ◽  
Alastair J Noyce
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Big Data ◽  
Clinical Practice ◽  
Clinical Data ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Basic Principles ◽  
Biological Neural Networks

Modern clinical practice requires the integration and interpretation of ever-expanding volumes of clinical data. There is, therefore, an imperative to develop efficient ways to process and understand these large amounts of data. Neurologists work to understand the function of biological neural networks, but artificial neural networks and other forms of machine learning algorithm are likely to be increasingly encountered in clinical practice. As their use increases, clinicians will need to understand the basic principles and common types of algorithm. We aim to provide a coherent introduction to this jargon-heavy subject and equip neurologists with the tools to understand, critically appraise and apply insights from this burgeoning field.

scholarly journals Contrastive Similarity Matching for Supervised Learning

2021 ◽  
pp. 1-29
Author(s):  
Shanshan Qin ◽  
Nayantara Mudur ◽  
Cengiz Pehlevan
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Learning Algorithm ◽  
Similarity Matrix ◽  
Credit Assignment ◽  
Similarity Matching ◽  
Hebbian Plasticity ◽  
Ventral Visual Pathway ◽  
Feedback Connections ◽  
Specific Learning

We propose a novel biologically plausible solution to the credit assignment problem motivated by observations in the ventral visual pathway and trained deep neural networks. In both, representations of objects in the same category become progressively more similar, while objects belonging to different categories become less similar. We use this observation to motivate a layer-specific learning goal in a deep network: each layer aims to learn a representational similarity matrix that interpolates between previous and later layers. We formulate this idea using a contrastive similarity matching objective function and derive from it deep neural networks with feedforward, lateral, and feedback connections and neurons that exhibit biologically plausible Hebbian and anti-Hebbian plasticity. Contrastive similarity matching can be interpreted as an energy-based learning algorithm, but with significant differences from others in how a contrastive function is constructed.

Turbine Engine Modeling Using Temporal Neural Networks for Incipient Fault Detection and Diagnosis

2004 ◽  
Author(s):  
Sunil Menon ◽  
O¨nder Uluyol ◽  
Deepanker Gupta
Keyword(s):  
Neural Networks ◽  
Fault Detection ◽  
Fault Detection And Diagnosis ◽  
Sensor Data ◽  
Turbine Engine ◽  
Echo State Networks ◽  
Diagnosis Method ◽  
Incipient Fault ◽  
Detection And Diagnosis ◽  
Incipient Fault Detection

We present a method of fault detection and diagnosis in turbine engines using temporal neural networks. Temporal neural networks allow us to represent the complete engine operating range by complementing the first-principle models which are usually restricted to takeoff and cruise phases. Because faults that are manifest only in particular phases can be detected, complete coverage leads to more accurate anomaly detection and fault diagnosis systems. The time series sensor data from the engine is collected during particular aircraft flight phases such as startup, takeoff, cruise, and shutdown. We use the echo state network to develop an incipient fault detection and diagnosis system. Echo state networks have several advantages over conventional types of temporal neural networks, including accuracy and ease of training. We demonstrate the efficacy of using the echo state networks to focus on flight phases that are difficult to model. We present results of our fault detection and diagnosis method with actual propulsion engine transient flight data.

scholarly journals Diagnosis of Plant Diseases using Artificial Neural Network

2019 ◽  
Vol 8 (11) ◽  
pp. 1464-1467
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Plant Diseases ◽  
Economic Losses ◽  
Fruit Crops ◽  
Agricultural Business ◽  
Complex Foundation

Pomegranate is one of India's most commonly cultivated fruit crops. manual expert observations are being used to detect leaf diseases that take longer time for further prevention. Fruit diseases are causing devastating disadvantages in worldwide agricultural business economic losses in production .in this journal, the answer is proposed and valid by experiment for the identification and classification of fruit disorders. The objective of proposed work is to analyze the illness utilizing picture preparing and artificial intelligence techniques on pictures of pomegranate plant leaf. In the proposed framework, pomegranate leaf picture with complex foundation is taken as input. Then pomegranate leaf ailment division is finished utilizing K-means clustering. The infected segment from portioned pictures is recognized. Best results have been seen when neural networks with a RBFN is used for a classification.

P–135 Artificial neural networks (ANNs) for live birth prediction in frozen embryo transfers: the strength of post-warmed blastocyst dynamics

2021 ◽  
Vol 36 (Supplement_1) ◽  
Author(s):  
L Alegre ◽  
L Bori ◽  
A Coello ◽  
A S Ferreira ◽  
J C Rocha ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Embryo Transfer ◽  
Live Birth ◽  
Zona Pellucida ◽  
Learning Algorithm ◽  
Inner Cell Mass ◽  
Cell Mass ◽  
Implantation Rate ◽  
Frozen Embryo Transfers

Abstract Study question Does the post-warmed blastocyst dynamics have an impact over the likelihood of achieving a live birth? Summary answer Variables related to dynamics of vitrified/warmed blastocysts have shown a greater effect on the live birth prediction than only embryo morphological quality through artificial intelligence. What is known already Morphological dynamics of vitrified/warmed blastocysts were described by Coello et al., in 2017. The investigated markers were the thickness of zona pellucida (µm) and blastocysts area (µm2) after warming and before transfer, the area of the inner cell mass (µm2), time of initiation of reexpansion (in minutes), and presence of collapse or contraction. They found a correlation between blastocyst reexpansion and implantation rate and developed a hierarchical model for implantation prediction. In our study, we evaluated the post-warmed blastocyst dynamics for live birth prediction by using novel artificial intelligence techniques. Study design, size, duration This retrospective analysis included 415 vitrified/warmed blastocysts with known live birth data. Blastocysts after warming were placed in EmbryoScope (Vitrolife) immediately until embryo transfer. Embryo evaluation and selection were performed by senior embryologists according to fresh blastocyst morphology (before vitrification). Then, parameters related to post-warmed blastocyst dynamics were calculated. Finally, these variables and the embryo morphological grade before the vitrification were used as input data for ANNs optimized with genetic algorithm for live birth prediction. Participants/materials, setting, methods Blastocysts were vitrified and warmed by the Cryotop method (Kitazato,Biopharma). During the period between the warming procedure and the embryo transfer, the following variables were measured with the drawing tools provided by the EmbryoViewer workstation: zona pellucida thinning (µm), blastocyst expansion (um) and the speed of these two events (µm/h). Finally, multilayer perceptron neural networks were trained with data of 331 embryos by using the backpropagation learning algorithm and tested with data of 84 embryos. Main results and the role of chance We trained and tested three architectures of ANNs with different input variables as follows: post-warmed variables (thinning of the zona pellucida, blastocyst expansion, thinning speed and expansion speed) and morphological grade (A, B or C) for ANN1, only post-warmed variables for ANN2 and only morphological grade for ANN3. The highest success rate when ANNs classified embryos as positive and negative live birth (LB+ and LB-) was achieved by combining post-warmed variables and morphological grade before embryo vitrification. The general accuracies for the blind tests were: 73.8% for ANN1, 66.7% for ANN2 and 71.4% for ANN3. Likewise, this combination achieved the highest AUC on test dataset to predict LB- (0.76 for ANN1, 0.74 for ANN2 and 0.67 for ANN3). However, the ANN2 trained with only post-warmed variables showed the best capacity to predict LB+ with an AUC of 0.73 (versus 0.46 for ANN1 and 0.5 for ANN3). Limitations, reasons for caution The main limitation is the subjectivity of manual annotations, although only one embryologist participated in this task. Wider implications of the findings: The dynamics of vitrified/warmed blastocysts prior to embryo transfer could be more relevant variables than the morphological quality on day 5 before the cryopreservation. The analysis of embryo behavior after warming could improve clinical outcomes in frozen embryo transfers. Trial registration number none

scholarly journals Programmable artificial intelligence machine for wave sensing and communications

2020 ◽  
Author(s):  
Tie Jun Cui ◽  
Che Liu ◽  
qian ma ◽  
Zhangjie Luo ◽  
Qiaoru Hong ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Deep Learning ◽  
Integrated Circuits ◽  
Dynamic Range ◽  
Learning Algorithm ◽  
Human Life ◽  
Computer Algorithms ◽  
Beam Focusing ◽  
Learning Tasks

Abstract Artificial intelligence is facilitating human life in many aspects. Previous artificial intelligence has been mainly focused on computer algorithms (e.g. deep-learning and extreme-learning) and integrated circuits. Recently, all-optical diffractive deep neural networks (D2NN) were realized by using passive structures, which can perform complicated functions designed by computer-based neural networks at the light speed. However, once a passive D2NN architecture is fabricated, its function will be fixed. Here, we propose a programmable artificial intelligence machine (PAIM) that can execute various intellectual tasks by realizing hierarchical connections of brain neurons via a multi-layer digital-coding metasurface array. Integrated with two amplifier chips in each meta-atom, its transmission coefficient covers a dynamic range of 35 dB (from -40 dB to -5 dB), which is the basis to construct the reprogrammable physical layers of D2NN, in which the digital meta-atoms make the artificial neurons alive. We experimentally show that PAIM can handle various deep-learning tasks for wave sensing, including image classifications, mobile communication coder-decoder, and real-time multi-beam focusing. In particular, we propose a reinforcement learning algorithm for on-site learning and discrete optimization algorithm for digital coding, making PAIM have autonomous intelligence ability and perform self-learning tasks without the support of extra computer.

scholarly journals Photonic reservoir computing using delay dynamical systems

Photoniques ◽  
2020 ◽  
pp. 45-48
Author(s):  
Piotr Antonik ◽  
Serge Massar ◽  
Guy Van Der Sande
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Dynamical Systems ◽  
Recent Progress ◽  
Learning Algorithm ◽  
Reservoir Computing ◽  
Machine Learning Algorithm ◽  
Hardware Implementations ◽  
All Optical

The recent progress in artificial intelligence has spurred renewed interest in hardware implementations of neural networks. Reservoir computing is a powerful, highly versatile machine learning algorithm well suited for experimental implementations. The simplest highperformance architecture is based on delay dynamical systems. We illustrate its power through a series of photonic examples, including the first all optical reservoir computer and reservoir computers based on lasers with delayed feedback. We also show how reservoirs can be used to emulate dynamical systems. We discuss the perspectives of photonic reservoir computing.

scholarly journals The Determining Gender Using Facial Recognition Based On Neural Network With Backpropagation

2018 ◽  
Vol 2 (1) ◽  
pp. 53-61
Author(s):  
Fauziah Fauziah
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Learning Algorithm ◽  
Facial Recognition ◽  
Test Results ◽  
Average Value ◽  
Best Value ◽  
Facial Identification

One area of science that can apply facial recognition applications is artificial intelligence. The algorithms used in facial recognition are quite numerous and varied, but they all have the same three basic stages, face detection, facial extraction and facial recognition (Face Recognition) . Facial recognition applications using artificial intelligence as a major component, especially artificial neural networks for processing and facial identification are still not widely encountered. Ba ckpropagation is a learning algorithm to minimize the error rate by adjusting the weights based on the desired output and target differences. The test results of 30 images have the average value of mse is 0.14796 and the best value of mse on the test of man number 3 with mse value 0.1488 and mean 0.0047 while for the female number 2 with mse value 0.1497 and niali mean 0.0047.

scholarly journals Early-Stage Detection of Cancer in Breast Using Artificial Intelligence

2021 ◽  
Vol 11 (2) ◽  
pp. 2016-2028
Author(s):  
M.N. Vimal Kumar ◽  
S. Aakash Ram ◽  
C. Shobana Nageswari ◽  
C. Raveena ◽  
S. Rajan
Keyword(s):  
Breast Cancer ◽  
Artificial Intelligence ◽  
Deep Learning ◽  
Learning Algorithm ◽  
Early Stage ◽  
Deep Learning Algorithm ◽  
Common Cancer ◽  
Abnormal Cells ◽  
Detection And Diagnosis ◽  
Early Detection And Diagnosis

One of the deadly diseases among humans is Cancer, which occurs almost anywhere in the human body. Cancer is caused by the cells that spread into the surrounding tissues by dividing itself uncontrollably. Breast Cancer is the most common cancer among women. Early detection and diagnosis of breast cancer are treatable and curable. Many women have no symptoms for this cancer at an early stage. The abnormal cells in the breast will risk for the development of breast cancer. So, it is important for women to regularly examine their breast. Technologies can be utilized in a smarter way with Artificial Intelligence techniques to assist the women during their examination of the breast at their living place to avoid the risk of breast cancer. The main aim is to develop a lowcost self-examining device for the detection of breast cancer and abnormality in the breast using an efficient optical method, Deep-learning algorithm and Internet of Things.

scholarly journals Applying artificial neural networks in slope stability related phenomena

2016 ◽  
Vol 47 (4) ◽  
pp. 1901
Author(s):  
P. Tsangaratos ◽  
A. Benardos
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Slope Stability ◽  
Landslide Susceptibility ◽  
Learning Algorithm ◽  
The Past ◽  
Artificial Neural ◽  
Gis Environment ◽  
Specific Learning ◽  
The Way

Over the past years, Artificial Neural Networks (ANN) have been successfully used for the modelling in a great number of geoscience applications. In this paper we discuss the architecture and the way ANN work, presenting a specific learning algorithm which has been applied in the estimation of landslide susceptibility within a GIS environment.

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