scholarly journals IMUMETER—A Convolution Neural Network-Based Sensor for Measurement of Aircraft Ground Performance

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4726
Author(s):  
Jarosław Pytka ◽  
Piotr Budzyński ◽  
Paweł Tomiło ◽  
Joanna Michałowska ◽  
Ernest Gnapowski ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Convolution Neural Network ◽  
Flight Tests ◽  
Motion Measurement ◽  
Aircraft Flight ◽  
Acceleration Data ◽  
Measurement Results ◽  
Measurement And Analysis ◽  
Ground Distance

The paper presents the development of the IMUMETER sensor, designed to study the dynamics of aircraft movement, in particular, to measure the ground performance of the aircraft. A motivation of this study was to develop a sensor capable of airplane motion measurement, especially for airfield performance, takeoff and landing. The IMUMETER sensor was designed on the basis of the method of artificial neural networks. The use of a neural network is justified by the fact that the automation of the measurement of the airplane’s ground distance during landing based on acceleration data is possible thanks to the recognition of the touchdown and stopping points, using artificial intelligence. The hardware is based on a single-board computer that works with the inertial navigation platform and a satellite navigation sensor. In the development of the IMUMETER device, original software solutions were developed and tested. The paper describes the development of the Convolution Neural Network, including the learning process based on the measurement results during flight tests of the PZL 104 Wilga 35A aircraft. The ground distance of the test airplane during landing on a grass runway was calculated using the developed neural network model. Additionally included are exemplary measurements of the landing distance of the test airplane during landing on a grass runway. The results obtained in this study can be useful in the development of artificial intelligence-based sensors, especially those for the measurement and analysis of aircraft flight dynamics.

Recent Advances in Medical Image Processing

2020 ◽  
pp. 1-14
Author(s):  
Zhen Huang ◽  
Qiang Li ◽  
Ju Lu ◽  
Junlin Feng ◽  
Jiajia Hu ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Image Processing ◽  
Medical Imaging ◽  
Medical Image ◽  
Medical Image Processing ◽  
Convolution Neural Network ◽  
Artificial Intelligence Technology ◽  
Deep Convolution Neural Network

<b><i>Background:</i></b> Application and development of the artificial intelligence technology have generated a profound impact in the field of medical imaging. It helps medical personnel to make an early and more accurate diagnosis. Recently, the deep convolution neural network is emerging as a principal machine learning method in computer vision and has received significant attention in medical imaging. <b><i>Key Message:</i></b> In this paper, we will review recent advances in artificial intelligence, machine learning, and deep convolution neural network, focusing on their applications in medical image processing. To illustrate with a concrete example, we discuss in detail the architecture of a convolution neural network through visualization to help understand its internal working mechanism. <b><i>Summary:</i></b> This review discusses several open questions, current trends, and critical challenges faced by medical image processing and artificial intelligence technology.

scholarly journals Elements of an algorithm for optimizing a parameter-structural neural network

2016 ◽  
Vol 101 (1) ◽  
pp. 27-35 ◽  
Author(s):  
Maria Mrówczyńska
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Optimization Methods ◽  
Group Method ◽  
Data Handling ◽  
Training Process ◽  
Measurement Results ◽  
Processing Information

Abstract The field of processing information provided by measurement results is one of the most important components of geodetic technologies. The dynamic development of this field improves classic algorithms for numerical calculations in the aspect of analytical solutions that are difficult to achieve. Algorithms based on artificial intelligence in the form of artificial neural networks, including the topology of connections between neurons have become an important instrument connected to the problem of processing and modelling processes. This concept results from the integration of neural networks and parameter optimization methods and makes it possible to avoid the necessity to arbitrarily define the structure of a network. This kind of extension of the training process is exemplified by the algorithm called the Group Method of Data Handling (GMDH), which belongs to the class of evolutionary algorithms. The article presents a GMDH type network, used for modelling deformations of the geometrical axis of a steel chimney during its operation.

scholarly journals Self-repairing mechanical components using artificial Intelligence

2021 ◽  
Vol XXIV (1) ◽  
pp. 17-28
Author(s):  
PLEȘA Mihail Iulian
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Cellular Automata ◽  
Convolution Neural Network ◽  
Mechanical Component ◽  
Small Seed ◽  
Ca Model ◽  
Mechanical Components

In this paper, we study the applicability of artificial intelligence for designing mechanical components that can repair themselves. We use the Cellular Automata (CA) model implemented as a Convolution Neural Network (CNN) to simulate the automatic growth and repair of a mechanical component from a small seed. Concretely, we start with an empty 2D grid of cells. Using the CNN, the cells will learn to self-organize into the image of a mechanical component. We simulate the damage to the component by deleting some parts of the imagine and show how they are automatically regenerated.

scholarly journals Deep Convolution Neural Network Based Artificial Intelligence Improves Diagnosis of Thyroid Scintigraphy for Thyrotoxicosis: a Dual Center Study

2020 ◽  
Author(s):  
Pei Yang ◽  
Yong Pi ◽  
Tao He ◽  
Ke Zhou ◽  
Xiao Zhong ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Diagnostic Accuracy ◽  
Convolution Neural Network ◽  
Thyroid Scintigraphy ◽  
West China Hospital ◽  
West China ◽  
Contrastive Study ◽  
External Data ◽  
Deep Convolution Neural Network

Abstract Background: 99mTc-pertechnetate thyroid scintigraphy is a valid avenue for distinguishing causes of thyrotoxicosis in the clinic, but the interpretation of thyroid scintigraphic images is subjected with significant variation among different inter-observers. We aim to develop an artificial intelligence (AI) system to improve the diagnosis of thyrotoxicosis.Materials and methods: We constructed an AI model based on a deep neural network with 2468 thyroid scintigraphic images collected from West China Hospital, and evaluated the diagnostic accuracy for classifying four patterns of thyrotoxicosis: ‘diffusely increased,’ ‘diffusely decreased,’ ‘focal increased,’ and ‘heterogeneous uptake.’ Then, we compared the diagnostic performance of the AI model and five physicians with 200 testing cohorts from two centers.Results: We constructed the AI model, which has the best performance in internal database validation based on four kinds of standout pre-trained networks. This AI model achieves satisfactory performance in classifying four patterns of thyrotoxicosis with an overall accuracy of 91.92% for internal and 86.75% for external data validation. In the following contrastive study, the AI model represented improved diagnostic accuracy and consistency than 5 physicians for interpreting data from West China Hospital (88% vs. 66~73%) and Panzhihua Central Hospital (83% vs. 53%~79%), respectively.Conclusion: Deep convolution neural network based AI model represented considerable performance in classifying four patterns of thyroid scintigraphic images; this may help physicians diagnose causes of thyrotoxicosis and reduced the physicians’ error rate.

scholarly journals Artificial Intelligence – State of Art Convolution Neural Network Architectures in a Nutshell

2019 ◽  
Vol 8 (11S) ◽  
pp. 1278-1280
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Convolution Neural Network ◽  
Network Architectures ◽  
Deep Architecture ◽  
Different Types ◽  
Neuro Imaging ◽  
Brain Data ◽  
Configuration Changes ◽  
The Brain

It is a well-known fact that all the Artificial Intelligence (AI)researches happening across multiple verticals such as Neuro Imaging, Computer Vision, Deep learning etc point to one master goal of modelling the human brain function by understanding how each part of the brain works. The Convolution neural network (CNN) is one of best deep architecture suitable to handle variety of inputs. In this paper we explore the different types of input data the CNN deep architecture can process and some of the CNN configuration changes that has proved good Accuracy. We have highlighted those specialized CNN architectures along with different types of data inputs they handle including the Functional Magnetic Resonance (fMRI) Neuro Image brain data input.

scholarly journals Automated Verification of Structural Engineering Assembly using Convolution Neural Network

2020 ◽  
Vol 9 (5) ◽  
pp. 1319-1323
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Structural Engineering ◽  
Civil Engineering ◽  
Convolution Neural Network ◽  
Automated Verification ◽  
Manual Verification ◽  
Design And Construction

Artificial Intelligence has mostly penetrated in every field of technology and our lifestyle in numerous ways. The contribution of AI in the field of Civil engineering which mainly focuses on planning, design and construction is enormous. The main objective of this work is to develop a system that will automate the process of detecting errors in the engineering plans or drawings of structures. The work adapts convolution neural network technique with the help of Inception V3 model to automate detecting of multiple errors using Artificial Intelligence. AI technique has proven to be more effective, accurate and less time consuming against the existing manual verification technique

scholarly journals Diagnosa COVID-19 Chest X-Ray Dengan Convolution Neural Network Arsitektur Resnet-152

2021 ◽  
Vol 2 (1) ◽  
pp. 26-33
Author(s):  
Widi Hastomo
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Deep Learning ◽  
Viral Pneumonia ◽  
Convolution Neural Network ◽  
Normal Lung ◽  
X Ray ◽  
Image Dataset ◽  
Chest X Ray ◽  
Medical Aids

The availability of medical aids in adequate quantities is very much needed to assist the work of the medical staff in dealing with the very large number of Covid patients. Artificial Intelligence (AI) with the Deep Learning (DL) method, especially the Convolution Neural Network (CNN), is able to diagnose Chest X-ray images generated by the Computer Tomography Scanner (C.T. Scan) against certain diseases (Covid). Resnet Version-152 architecture was used in this study to train a dataset of 10.300 images, consisting of 4 classifications namely covid, normal, lung opacity with 3,000 images each and viral pneumonia 1,000 images. The results of the study with 50 epoch training obtained very good values for the accuracy of training and validation of 95.5% and 91.8%, respectively. The test with 10.300 image dataset obtained 98% accuracy testing, with the precision of each class being Covid (99%), Lung_Opacity (99%), Normal (98%) and Viral pneumonia (98%). 

Improving the effectiveness of traditional education based on computer artificial intelligence and neural network system

2020 ◽  
pp. 1-11
Author(s):  
Wenjuan Ma ◽  
Xuesi Zhao ◽  
Yuxiu Guo
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Education Reform ◽  
Traditional Education ◽  
Machine Learning Algorithms ◽  
Local Optimum ◽  
Optimization Strategy ◽  
Fish School ◽  
Tlbo Algorithm ◽  
Neural Network System

The application of artificial intelligence and machine learning algorithms in education reform is an inevitable trend of teaching development. In order to improve the teaching intelligence, this paper builds an auxiliary teaching system based on computer artificial intelligence and neural network based on the traditional teaching model. Moreover, in this paper, the optimization strategy is adopted in the TLBO algorithm to reduce the running time of the algorithm, and the extracurricular learning mechanism is introduced to increase the adjustable parameters, which is conducive to the algorithm jumping out of the local optimum. In addition, in this paper, the crowding factor in the fish school algorithm is used to define the degree or restraint of teachers’ control over students. At the same time, students in the crowded range gather near the teacher, and some students who are difficult to restrain perform the following behavior to follow the top students. Finally, this study builds a model based on actual needs, and designs a control experiment to verify the system performance. The results show that the system constructed in this paper has good performance and can provide a theoretical reference for related research.

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