Automated terrain mapping based on mask R-CNN neural network

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Anton Saveliev ◽  
Egor Aksamentov ◽  
Evgenii Karasev
Keyword(s):  
Neural Network ◽  
Total Duration ◽  
Image Resolution ◽  
Terrain Mapping ◽  
Computing Power ◽  
Content Type ◽  
Novel Approach ◽  
Application Architecture ◽  
Aerial Vehicle ◽  
Robotic Vehicles

PurposeThe purpose of this paper is to analyze the development of a novel approach for automated terrain mapping a robotic vehicles path tracing.Design/methodology/approachThe approach includes stitching of images, obtained from unmanned aerial vehicle, based on ORB descriptors, into an orthomosaic image and the GPS-coordinates are binded to the corresponding pixels of the map. The obtained image is fed to a neural network MASK R-CNN for detection and classification regions, which are potentially dangerous for robotic vehicles motion. To visualize the obtained map and obstacles on it, the authors propose their own application architecture. Users can any time edit the present areas or add new ones, which are not intended for robotic vehicles traffic. Then the GPS-coordinates of these areas are passed to robotic vehicles and the optimal route is traced based on this dataFindingsThe developed approach allows revealing impassable regions on terrain map and associating them with GPS-coordinates, whereas these regions can be edited by the user.Practical implicationsThe total duration of the algorithm, including the step with Mask R-CNN network on the same dataset of 120 items was 7.5 s.Originality/valueCreating an orthophotomap from 120 images with image resolution of 470 × 425 px requires less than 6 s on a laptop with moderate computing power, what justifies using such algorithms in the field without any powerful and expensive hardware.

Hybrid intelligent adaptive controller for tiltrotor UAV

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jafar Tavoosi
Keyword(s):  
Neural Network ◽  
Position Control ◽  
Control Method ◽  
Hybrid Control ◽  
Adaptive Controller ◽  
Content Type ◽  
Flight Mode ◽  
Horizontal Flight ◽  
Aerial Vehicle ◽  
Model Reference Adaptive

PurposeIn this paper, an innovative hybrid intelligent position control method for vertical take-off and landing (VTOL) tiltrotor unmanned aerial vehicle (UAV) is proposed. So the more accurate the reference position signals tracking, the proposed control system will be better.Design/methodology/approachIn the proposed method, for the vertical flight mode, first the model reference adaptive controller (MRAC) operates and for the horizontal flight, the model predictive control (MPC) will operate. Since the linear model is used for both of these controllers and naturally has an error compared to the real nonlinear model, a neural network is used to compensate for them. So the main novelties of this paper are a new hybrid control design (MRAC & MPC) and a neural network-based compensator for tiltrotor UAV.FindingsThe proper performance of the proposed control method in the simulation results is clear. Also the results showed that the role of compensator is very important and necessary, especially in extreme speed wind conditions and uncertain parameters.Originality/valueNovel hybrid control method. 10;-New method to use neural network as compensator in an UAV.

Extraction of microwave FET noise wave temperatures by using a novel neural approach

2016 ◽  
Vol 35 (1) ◽  
pp. 339-349 ◽  
Author(s):  
Vladica Ðorđević ◽  
Zlatica Marinković ◽  
Vera Marković ◽  
Olivera Pronić-Rančić
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Design Methodology ◽  
Direct Extraction ◽  
Content Type ◽  
Efficient Approach ◽  
Noise Parameters ◽  
Novel Approach ◽  
Artificial Neural Network Ann ◽  
The Given

Purpose – The purpose of this paper is the development of an efficient approach for extraction of the microwave FET noise wave temperatures. Design/methodology/approach – The proposed approach is based on an artificial neural network (ANN) trained to determine the noise wave temperatures from the given measured transistor noise parameters. Findings – The presented approach enables not only efficient, but also an accurate direct extraction of the noise wave temperatures. This is confirmed by the validation of the proposed approach that is done by comparison of the transistor noise parameters obtained using the extracted noise wave temperatures with the measured noise parameters. Originality/value – Application of ANN is a novel approach to extract the noise wave temperatures, which provides more efficient microwave FET noise wave modeling.

A Novel Approach Using Convolutional Neural Network to Reconstruct Image resolution

2019 ◽  
Vol 9 (5) ◽  
pp. p8999
Author(s):  
Abdul Karim Armah ◽  
Michael Kwame Ansong ◽  
Samson Hansen Sackey ◽  
Ninjerdene Bulgan
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Resolution ◽  
Novel Approach

Improved neural network-based sensor fault detection and estimation strategy for an autonomous aerial vehicle

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mati Ullah ◽  
Chunhui Zhao ◽  
Hamid Maqsood ◽  
Mahmood Ul Hassan ◽  
Muhammad Humayun
Keyword(s):  
Neural Network ◽  
Fault Detection ◽  
Radial Basis Function ◽  
Basis Function ◽  
Sliding Mode ◽  
The Other ◽  
Sensor Faults ◽  
Content Type ◽  
Detection And Estimation ◽  
Aerial Vehicle

PurposeThis paper aims to design an adaptive nonlinear strategy capable of timely detection and reconstruction of faults in the attitude’s sensors of an autonomous aerial vehicle with greater accuracy concerning other conventional approaches in the literature.Design/methodology/approachThe proposed scheme integrates a baseline nonlinear controller with an improved radial basis function neural network (IRBFNN) to detect different kinds of anomalies and failures that may occur in the attitude’s sensors of an autonomous aerial vehicle. An integral sliding mode concept is used as auto-tune weight update law in the IRBFNN instead of conventional weight update laws to optimize its learning capability without computational complexities. The simulations results and stability analysis validate the promising contributions of the suggested methodology over the other conventional approaches.FindingsThe performance of the proposed control algorithm is compared with the conventional radial basis function neural network (RBFNN), multi-layer perceptron neural network (MLPNN) and high gain observer (HGO) for a quadrotor vehicle suffering from various kinds of faults, e.g. abrupt, incipient and intermittent. From the simulation results obtained, it is found that the proposed algorithm’s performance in faults detection and estimation is relatively better than the rest of the methodologies.Practical implicationsFor the improvement in the stability and safety of an autonomous aerial vehicle during flight operations, quick identification and reconstruction of attitude’s sensor faults and failures always play a crucial role. Efficient fault detection and estimation scheme are considered indispensable for an error-free and safe flight mission of an autonomous aerial vehicle.Originality/valueThe proposed scheme introduces RBFNN techniques to detect and estimate the quadrotor attitude’s sensor faults and failures efficiently. An integral sliding mode effect is used as the network’s backpropagation law to automatically modify its learning parameters accordingly, thereby speeding up the learning capabilities as compared to the conventional neural network backpropagation laws. Compared with the other investigated techniques, the proposed strategy achieve remarkable results in the detection and estimation of various faults.

Developing an Artificial Intelligence Project in your Radiology Department

2020 ◽  
Vol 2 ◽  
pp. 58-61 ◽  
Author(s):  
Syed Junaid ◽  
Asad Saeed ◽  
Zeili Yang ◽  
Thomas Micic ◽  
Rajesh Botchu
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Health Care ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Radiology Department ◽  
Short Article ◽  
Computing Power ◽  
Road Map ◽  
Key Concepts

The advances in deep learning algorithms, exponential computing power, and availability of digital patient data like never before have led to the wave of interest and investment in artificial intelligence in health care. No radiology conference is complete without a substantial dedication to AI. Many radiology departments are keen to get involved but are unsure of where and how to begin. This short article provides a simple road map to aid departments to get involved with the technology, demystify key concepts, and pique an interest in the field. We have broken down the journey into seven steps; problem, team, data, kit, neural network, validation, and governance.

Low-Cost Hardware Implementation of Human Blood Identification Device Using Feed-Forward Artificial Neural Network on FPGA

2018 ◽  
Author(s):  
Rizki Eka Putri ◽  
Denny Darlis
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Low Cost ◽  
Image Resolution ◽  
Blood Type ◽  
Feed Forward ◽  
Testing Facility ◽  
System Input ◽  
Artificial Neural ◽  
Short Time

This article was under review for ICELTICS 2018 -- In the medical world there is still service dissatisfaction caused by lack of blood type testing facility. If the number of tested blood arise, a lot of problems will occur so that electronic devices are needed to determine the blood type accurately and in short time. In this research we implemented an Artificial Neural Network on Xilinx Spartan 3S1000 Field Programable Gate Array using XSA-3S Board to identify the blood type. This research uses blood sample image as system input. VHSIC Hardware Discription Language is the language to describe the algorithm. The algorithm used is feed-forward propagation of backpropagation neural network. There are 3 layers used in design, they are input, hidden1, and output. At hidden1layer has two neurons. In this study the accuracy of detection obtained are 92%, 92%, 92%, 90% and 86% for 32x32, 48x48, 64x64, 80x80, and 96x96 pixel blood image resolution, respectively.

A cell-based smoothed point interpolation method (CS-PIM) for 2D thermoelastic problems

2017 ◽  
Vol 27 (6) ◽  
pp. 1249-1265 ◽  
Author(s):  
Yijun Liu ◽  
Guiyong Zhang ◽  
Huan Lu ◽  
Zhi Zong
Keyword(s):  
Thermal Stresses ◽  
Interpolation Method ◽  
Content Type ◽  
Novel Approach ◽  
Convergence Results ◽  
Point Interpolation Method ◽  
Point Interpolation ◽  
Thermoelastic Problems ◽  
Gradient Smoothing ◽  
Practical Implications

Purpose Due to the strong reliance on element quality, there exist some inherent shortcomings of the traditional finite element method (FEM). The model of FEM behaves overly stiff, and the solutions of automated generated linear elements are generally of poor accuracy about especially gradient results. The proposed cell-based smoothed point interpolation method (CS-PIM) aims to improve the results accuracy of the thermoelastic problems via properly softening the overly-stiff stiffness. Design/methodology/approach This novel approach is based on the newly developed G space and weakened weak (w2) formulation, and of which shape functions are created using the point interpolation method and the cell-based gradient smoothing operation is conducted based on the linear triangular background cells. Findings Owing to the property of softened stiffness, the present method can generally achieve better accuracy and higher convergence results (especially for the temperature gradient and thermal stress solutions) than the FEM does by using the simplest linear triangular background cells, which has been examined by extensive numerical studies. Practical implications The CS-PIM is capable of producing more accurate results of temperature gradients as well as thermal stresses with the automated generated and unstructured background cells, which make it a better candidate for solving practical thermoelastic problems. Originality/value It is the first time that the novel CS-PIM was further developed for solving thermoelastic problems, which shows its tremendous potential for practical implications.

scholarly journals Automated Confirmation of Protein Annotation Using NLP and the UniProtKB Database

2020 ◽  
Vol 11 (1) ◽  
pp. 24
Author(s):  
Jin Tao ◽  
Kelly Brayton ◽  
Shira Broschat
Keyword(s):  
Language Processing ◽  
Fine Tuning ◽  
Support Vector ◽  
Protein Annotation ◽  
Computing Power ◽  
Journal Publication ◽  
Novel Approach ◽  
Uniprotkb Database ◽  
Public Repositories ◽  
Annotation Errors

Advances in genome sequencing technology and computing power have brought about the explosive growth of sequenced genomes in public repositories with a concomitant increase in annotation errors. Many protein sequences are annotated using computational analysis rather than experimental verification, leading to inaccuracies in annotation. Confirmation of existing protein annotations is urgently needed before misannotation becomes even more prevalent due to error propagation. In this work we present a novel approach for automatically confirming the existence of manually curated information with experimental evidence of protein annotation. Our ensemble learning method uses a combination of recurrent convolutional neural network, logistic regression, and support vector machine models. Natural language processing in the form of word embeddings is used with journal publication titles retrieved from the UniProtKB database. Importantly, we use recall as our most significant metric to ensure the maximum number of verifications possible; results are reported to a human curator for confirmation. Our ensemble model achieves 91.25% recall, 71.26% accuracy, 65.19% precision, and an F1 score of 76.05% and outperforms the Bidirectional Encoder Representations from Transformers for Biomedical Text Mining (BioBERT) model with fine-tuning using the same data.

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