scholarly journals Drone Trajectory Segmentation for Real-Time and Adaptive Time-Of-Flight Prediction

Drones ◽  
2021 ◽  
Vol 5 (3) ◽  
pp. 62
Author(s):  
Claudia Conte ◽  
Giorgio de Alteriis ◽  
Rosario Schiano Lo Moriello ◽  
Domenico Accardo ◽  
Giancarlo Rufino
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Traffic Management ◽  
Flight Time ◽  
Machine Learning Techniques ◽  
Wind Condition ◽  
Learning Techniques ◽  
Management Procedures ◽  
Adaptive Time ◽  
Trained Neural Network

This paper presents a method developed to predict the flight-time employed by a drone to complete a planned path adopting a machine-learning-based approach. A generic path is cut in properly designed corner-shaped standard sub-paths and the flight-time needed to travel along a standard sub-path is predicted employing a properly trained neural network. The final flight-time over the complete path is computed summing the partial results related to the standard sub-paths. Real drone flight-tests were performed in order to realize an adequate database needed to train the adopted neural network as a classifier, employing the Bayesian regularization backpropagation algorithm as training function. For the network, the relative angle between two sides of a corner and the wind condition are the inputs, while the flight-time over the corner is the output parameter. Then, generic paths were designed and performed to test the method. The total flight-time as resulting from the drone telemetry was compared with the flight-time predicted by the developed method based on machine learning techniques. At the end of the paper, the proposed method was demonstrated as effective in predicting possible collisions among drones flying intersecting paths, as a possible application to support the development of unmanned traffic management procedures.

Application of Machine Learning Techniques to Predict Software Reliability

2012 ◽  
pp. 237-253
Author(s):  
Ramakanta Mohanty ◽  
V. Ravi ◽  
M. R. Patra
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Software Reliability ◽  
Fuzzy Inference ◽  
Back Propagation ◽  
Multivariate Adaptive Regression Splines ◽  
Machine Learning Techniques ◽  
Group Method ◽  
Learning Techniques ◽  
Trained Neural Network

In this paper, the authors employed machine learning techniques, specifically, Back propagation trained neural network (BPNN), Group method of data handling (GMDH), Counter propagation neural network (CPNN), Dynamic evolving neuro–fuzzy inference system (DENFIS), Genetic Programming (GP), TreeNet, statistical multiple linear regression (MLR), and multivariate adaptive regression splines (MARS), to accurately forecast software reliability. Their effectiveness is demonstrated on three datasets taken from literature, where performance is compared in terms of normalized root mean square error (NRMSE) obtained in the test set. From rigorous experiments conducted, it was observed that GP outperformed all techniques in all datasets, with GMDH coming a close second.

Application of Machine Learning Techniques to Predict Software Reliability

2012 ◽  
pp. 354-370
Author(s):  
Ramakanta Mohanty ◽  
V. Ravi ◽  
M. R. Patra
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Software Reliability ◽  
Fuzzy Inference ◽  
Back Propagation ◽  
Multivariate Adaptive Regression Splines ◽  
Machine Learning Techniques ◽  
Group Method ◽  
Learning Techniques ◽  
Trained Neural Network

In this paper, the authors employed machine learning techniques, specifically, Back propagation trained neural network (BPNN), Group method of data handling (GMDH), Counter propagation neural network (CPNN), Dynamic evolving neuro–fuzzy inference system (DENFIS), Genetic Programming (GP), TreeNet, statistical multiple linear regression (MLR), and multivariate adaptive regression splines (MARS), to accurately forecast software reliability. Their effectiveness is demonstrated on three datasets taken from literature, where performance is compared in terms of normalized root mean square error (NRMSE) obtained in the test set. From rigorous experiments conducted, it was observed that GP outperformed all techniques in all datasets, with GMDH coming a close second.

scholarly journals Detection and Severity Evaluation of Combined Rail Defects Using Deep Learning

Vibration ◽  
2021 ◽  
Vol 4 (2) ◽  
pp. 341-356
Author(s):  
Jessada Sresakoolchai ◽  
Sakdirat Kaewunruen
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Machine Learning Techniques ◽  
Rolling Stock ◽  
Raw Data ◽  
Learning Techniques ◽  
Combined Defects

Various techniques have been developed to detect railway defects. One of the popular techniques is machine learning. This unprecedented study applies deep learning, which is a branch of machine learning techniques, to detect and evaluate the severity of rail combined defects. The combined defects in the study are settlement and dipped joint. Features used to detect and evaluate the severity of combined defects are axle box accelerations simulated using a verified rolling stock dynamic behavior simulation called D-Track. A total of 1650 simulations are run to generate numerical data. Deep learning techniques used in the study are deep neural network (DNN), convolutional neural network (CNN), and recurrent neural network (RNN). Simulated data are used in two ways: simplified data and raw data. Simplified data are used to develop the DNN model, while raw data are used to develop the CNN and RNN model. For simplified data, features are extracted from raw data, which are the weight of rolling stock, the speed of rolling stock, and three peak and bottom accelerations from two wheels of rolling stock. In total, there are 14 features used as simplified data for developing the DNN model. For raw data, time-domain accelerations are used directly to develop the CNN and RNN models without processing and data extraction. Hyperparameter tuning is performed to ensure that the performance of each model is optimized. Grid search is used for performing hyperparameter tuning. To detect the combined defects, the study proposes two approaches. The first approach uses one model to detect settlement and dipped joint, and the second approach uses two models to detect settlement and dipped joint separately. The results show that the CNN models of both approaches provide the same accuracy of 99%, so one model is good enough to detect settlement and dipped joint. To evaluate the severity of the combined defects, the study applies classification and regression concepts. Classification is used to evaluate the severity by categorizing defects into light, medium, and severe classes, and regression is used to estimate the size of defects. From the study, the CNN model is suitable for evaluating dipped joint severity with an accuracy of 84% and mean absolute error (MAE) of 1.25 mm, and the RNN model is suitable for evaluating settlement severity with an accuracy of 99% and mean absolute error (MAE) of 1.58 mm.

scholarly journals Estimating Forest Carbon Fluxes Using Machine Learning Techniques Based on Eddy Covariance Measurements

2018 ◽  
Vol 10 (1) ◽  
pp. 203 ◽  
Author(s):  
Xianming Dou ◽  
Yongguo Yang ◽  
Jinhui Luo
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Eddy Covariance ◽  
Carbon Flux ◽  
Carbon Fluxes ◽  
Forest Carbon ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Inference System ◽  
Learning Techniques

Approximating the complex nonlinear relationships that dominate the exchange of carbon dioxide fluxes between the biosphere and atmosphere is fundamentally important for addressing the issue of climate change. The progress of machine learning techniques has offered a number of useful tools for the scientific community aiming to gain new insights into the temporal and spatial variation of different carbon fluxes in terrestrial ecosystems. In this study, adaptive neuro-fuzzy inference system (ANFIS) and generalized regression neural network (GRNN) models were developed to predict the daily carbon fluxes in three boreal forest ecosystems based on eddy covariance (EC) measurements. Moreover, a comparison was made between the modeled values derived from these models and those of traditional artificial neural network (ANN) and support vector machine (SVM) models. These models were also compared with multiple linear regression (MLR). Several statistical indicators, including coefficient of determination (R2), Nash-Sutcliffe efficiency (NSE), bias error (Bias) and root mean square error (RMSE) were utilized to evaluate the performance of the applied models. The results showed that the developed machine learning models were able to account for the most variance in the carbon fluxes at both daily and hourly time scales in the three stands and they consistently and substantially outperformed the MLR model for both daily and hourly carbon flux estimates. It was demonstrated that the ANFIS and ANN models provided similar estimates in the testing period with an approximate value of R2 = 0.93, NSE = 0.91, Bias = 0.11 g C m−2 day−1 and RMSE = 1.04 g C m−2 day−1 for daily gross primary productivity, 0.94, 0.82, 0.24 g C m−2 day−1 and 0.72 g C m−2 day−1 for daily ecosystem respiration, and 0.79, 0.75, 0.14 g C m−2 day−1 and 0.89 g C m−2 day−1 for daily net ecosystem exchange, and slightly outperformed the GRNN and SVM models. In practical terms, however, the newly developed models (ANFIS and GRNN) are more robust and flexible, and have less parameters needed for selection and optimization in comparison with traditional ANN and SVM models. Consequently, they can be used as valuable tools to estimate forest carbon fluxes and fill the missing carbon flux data during the long-term EC measurements.

scholarly journals Testing swampland conjectures with machine learning

2020 ◽  
Vol 80 (8) ◽  
Author(s):  
Nana Cabo Bizet ◽  
Cesar Damian ◽  
Oscar Loaiza-Brito ◽  
Damián Kaloni Mayorga Peña ◽  
J. A. Montañez-Barrera
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Vacuum Energy ◽  
Matrix Theory ◽  
Scalar Potential ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
De Sitter ◽  
Learning Techniques ◽  
Isotropic Torus

Abstract We consider Type IIB compactifications on an isotropic torus $$T^6$$T6 threaded by geometric and non geometric fluxes. For this particular setup we apply supervised machine learning techniques, namely an artificial neural network coupled to a genetic algorithm, in order to obtain more than sixty thousand flux configurations yielding to a scalar potential with at least one critical point. We observe that both stable AdS vacua with large moduli masses and small vacuum energy as well as unstable dS vacua with small tachyonic mass and large energy are absent, in accordance to the refined de Sitter conjecture. Moreover, by considering a hierarchy among fluxes, we observe that perturbative solutions with small values for the vacuum energy and moduli masses are favored, as well as scenarios in which the lightest modulus mass is much smaller than the corresponding AdS vacuum scale. Finally we apply some results on random matrix theory to conclude that the most probable mass spectrum derived from this string setup is that satisfying the Refined de Sitter and AdS scale conjectures.

scholarly journals Roundness prediction in centreless grinding using physics-enhanced machine learning techniques

2020 ◽  
Author(s):  
Hossein Safarzadeh ◽  
Marco Leonesio ◽  
Giacomo Bianchi ◽  
Michele Monno
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Regression Model ◽  
Hybrid Models ◽  
Physical Models ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Grinding Wheel ◽  
Wheel Wear ◽  
Learning Techniques

AbstractThis work proposes a model for suggesting optimal process configuration in plunge centreless grinding operations. Seven different approaches were implemented and compared: first principles model, neural network model with one hidden layer, support vector regression model with polynomial kernel function, Gaussian process regression model and hybrid versions of those three models. The first approach is based on an enhancement of the well-known numerical process simulation of geometrical instability. The model takes into account raw workpiece profile and possible wheel-workpiece loss of contact, which introduces an inherent limitation on the resulting profile waviness. Physical models, because of epistemic errors due to neglected or oversimplified functional relationships, can be too approximated for being considered in industrial applications. Moreover, in deterministic models, uncertainties affecting the various parameters are not explicitly considered. Complexity in centreless grinding models arises from phenomena like contact length dependency on local compliance, contact force and grinding wheel roughness, unpredicted material properties of the grinding wheel and workpiece, precision of the manual setup done by the operator, wheel wear and nature of wheel wear. In order to improve the overall model prediction accuracy and allow automated continuous learning, several machine learning techniques have been investigated: a Bayesian regularized neural network, an SVR model and a GPR model. To exploit the a priori knowledge embedded in physical models, hybrid models are proposed, where neural network, SVR and GPR models are fed by the nominal process parameters enriched with the roundness predicted by the first principle model. Those hybrid models result in an improved prediction capability.

scholarly journals A Machine Learning View on Momentum and Reversal Trading

Algorithms ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 170 ◽  
Author(s):  
Zhixi Li ◽  
Vincent Tam
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Stock Market ◽  
Short Term Memory ◽  
Predictive Ability ◽  
Trading Strategies ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Learning Approaches ◽  
Learning Techniques

Momentum and reversal effects are important phenomena in stock markets. In academia, relevant studies have been conducted for years. Researchers have attempted to analyze these phenomena using statistical methods and to give some plausible explanations. However, those explanations are sometimes unconvincing. Furthermore, it is very difficult to transfer the findings of these studies to real-world investment trading strategies due to the lack of predictive ability. This paper represents the first attempt to adopt machine learning techniques for investigating the momentum and reversal effects occurring in any stock market. In the study, various machine learning techniques, including the Decision Tree (DT), Support Vector Machine (SVM), Multilayer Perceptron Neural Network (MLP), and Long Short-Term Memory Neural Network (LSTM) were explored and compared carefully. Several models built on these machine learning approaches were used to predict the momentum or reversal effect on the stock market of mainland China, thus allowing investors to build corresponding trading strategies. The experimental results demonstrated that these machine learning approaches, especially the SVM, are beneficial for capturing the relevant momentum and reversal effects, and possibly building profitable trading strategies. Moreover, we propose the corresponding trading strategies in terms of market states to acquire the best investment returns.

A Study on Brain Tumor Detection and Segmentation Using Deep Learning Techniques

2020 ◽  
Vol 17 (4) ◽  
pp. 1925-1930
Author(s):  
Ambeshwar Kumar ◽  
R. Manikandan ◽  
Robbi Rahim
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Brain Tumor ◽  
Tumor Detection ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Learning Techniques ◽  
Convolution Algorithm ◽  
The Brain

It’s a new era technology in the field of medical engineering giving awareness about the various healthcare features. Deep learning is a part of machine learning, it is capable of handling high dimensional data and is efficient in concentrating on the right features. Tumor is an unbelievably complex disease: a multifaceted cell has more than hundred billion cells; each cell acquires mutation exclusively. Detection of tumor particles in experiment is easily done by MRI or CT. Brain tumors can also be detected by MRI, however, deep learning techniques give a better approach to segment the brain tumor images. Deep Learning models are imprecisely encouraged by information handling and communication designs in biological nervous system. Classification plays an significant role in brain tumor detection. Neural network is creating a well-organized rule for classification. To accomplish medical image data, neural network is trained to use the Convolution algorithm. Multilayer perceptron is intended for identification of a image. In this study article, the brain images are categorized into two types: normal and abnormal. This article emphasize the importance of classification and feature selection approach for predicting the brain tumor. This classification is done by machine learning techniques like Artificial Neural Networks, Support Vector Machine and Deep Neural Network. It could be noted that more than one technique can be applied for the segmentation of tumor. The several samples of brain tumor images are classified using deep learning algorithms, convolution neural network and multi-layer perceptron.

scholarly journals Performance comparison of machine learning techniques in sleep scoring based on wavelet features and neighboring component analysis

2018 ◽  
Author(s):  
Behrouz Alizadeh Savareh ◽  
Azadeh Bashiri ◽  
Ali Behmanesh ◽  
Gholam Hossein Meftahi ◽  
Boshra Hatef
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Dimension Reduction ◽  
Sleep Disorders ◽  
Component Analysis ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Sleep Scoring ◽  
Learning Techniques

Introduction: Sleep scoring is an important step in the treatment of sleep disorders. Manual annotation of sleep stages is time-consuming and experience-relevant and, therefore, needs to be done using machine learning techniques. methods: Sleep-edf polysomnography was used in this study as a dataset. Support Vector Machines and Artificial Neural Network performance were compared in sleep scoring using wavelet tree features and neighborhood component analysis. Results: Neighboring component analysis as a combination of linear and non-linear feature selection method had a substantial role in feature dimension reduction. Artificial neural network and support vector machine achieved 90.30% and 89.93% accuracy respectively. Discussion and Conclusion: Similar to the state of the art performance, introduced method in the present study achieved an acceptable performance in sleep scoring. Furthermore, its performance can be enhanced using a technique combined with other techniques in feature generation and dimension reduction. It is hoped that, in the future, intelligent techniques can be used in the process of diagnosing and treating sleep disorders.

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