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Item Open Access A tutorial on the Loewner-based system identification and structural health monitoring approach for mechanical systems.(Cranfield University, 2023-04-19 09:14) Dessena, GabrieleThe tutorial should be considered complementary material for the article "A Loewner-based system identification and structural health monitoring approach for mechanical systems" (DOI: 10.1155/2023/1891062). The tutorial illustrates, via a simple example, the capability of the Loewner Framework for System Identification and Structural Health Monitoring. The second goal of this tutorial is to make available a Loewner Framework program for MATLAB to the public based on the published work. Please cite [1-5] when using this software for your work or research, Thank you. This tutorial is linked to the following article: G. Dessena, M. Civera, L. Zanotti Fragonara, D. I. Ignatyev, J. F. Whidborne, A Loewner-based system identification and structural health monitoring approach for mechanical systems, Structural Control and Health Monitoring, Vol. 2023 (2023). (DOI: 10.1155/2023/1891062) The repository entry contains five files: 1. data_9dof.mat: the data for the tutorial model; 2. LF_id.m: the script for the LF-based System Identification; 3. loewner.m: the Loewner Matrix function; 4. LF_tutorial.mlx: the LF tutorial in MATLAB live mode 5. LF_tutorial.pdf: the LF tutorial in pdf. The program was created in MATLAB 2020a, compatibility with earlier or later versions is not guaranteed. References [1] G. Dessena, M. Civera, L. Zanotti Fragonara, D. I. Ignatyev, J. F. Whidborne, A Loewner-based system identification and structural health monitoring approach for mechanical systems, Structural Control and Health Monitoring, Vol. 2023 (2023). (DOI: 10.1155/2023/1891062) [2] G. Dessena, A Loewner-based system identification and structural health monitoring approach for mechanical systems, CORD repository (2021) (DOI: 10.17862/cranfield.rd.16636279) [3] A. J. Mayo, A. C. Antoulas, A framework for the solution of the generalized realization problem, Linear Algebra and its Applications 425, pp. 634–662 (2007). (DOI:10.1016/j.laa.2007.03.008) [4] S. Lefteriu, A. C. Antoulas, A New Approach to Modeling Multiport Systems From Frequency-Domain Data, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, Vol. 29, No. 1, pp. 14–27 (2010). (DOI: 10.1109/TCAD.2009.2034500) [5] S. Lefteriu, A. C. Ionita, A. C. Antoulas, Modeling Systems Based on Noisy Frequency and Time Domain Measurements, Lecture Notes in Control and Information Sciences, vol. 398/2010, 365-368 (2010). (DOI: 10.1007/978-3-540-93918-4_33)Item Open Access ASTRA_TOOLBOXES(Cranfield University, 2023-01-03 15:27) Andrea, BellomeASTRA_TOOLBOXES Author: Andrea Bellome Supervisors: Joan-Pau Sánchez Cuartielles, Leonard Felicetti, Stephen Kemble This project contains the following toolboxes: - AUTOMATE: AUTOmatic Multiple-gravity Assist with Tisserand Exploration - ASTRA: Automatic Swing-By TRAjectories - DYNAMIS: DYnamic programming for Asteroid MISsions Each toolbox comes with its own sub-folder. In each sub-folder, main .m scripts are included that represent test cases to use the toolboxes. Each script should be self-explanatory and easy to use. These are described briefly here. 1) st1_AUTOMATE This folder contains AUTOMATE toolbox. This is usefult to construct MGA sequences based upon Tisserand criterion. Both Solar System planets, Jovian and Saturn moons are available. In the case of moons' tour, single-objective dynamic programming (SODP) is used to find the optimal path. 2) st2_ASTRA This folder contains ASTRA toolbox. This can be used to optimize MGA sequences using either single-objective or multi-objective dynamic programming (SODP and MODP, respectively). 3) st3_DYNAMIS This folder contains DYNAMIS toolbox. This is useful to optimize MGA trajectory options that pass-by many asteroids using dynamic programming. Both single-objective and multi-objective dynamic programming are available (SODP and MODP, respectively).Item Open Access Code and data supporting 'A Comprehensive Analysis of Machine Learning and Deep Learning Models for Identifying Pilots' Mental States from Imbalanced Physiological Data'(Cranfield University, 2023-09-18 16:40) Alreshidi, Ibrahim; Moulitsas, Irene; Jenkins, Karl; Yadav, SatendraData: This folder contains: - A dataset called combined_df4, which contains the power spectral density features after employing SMOTE. - A dataset called combined_df5, which contains the power spectral density features after employing SMOTE and cosine similarity. Source code: This folder contains: - A jupyter notebook called AdaBoost.ipynb which was used to generate the results for the AdaBoost algorithm. - A jupyter notebook called CNN.ipynb which was used to generate the results for the CNN algorithm. - A jupyter notebook called CNN+LSTM.ipynb which was used to generate the results for the CNN+LSTMalgorithm. - A jupyter notebook called LSTM.ipynb which was used to generate the results for the LSTMalgorithm. - A jupyter notebook called FNN.ipynb which was used to generate the results for the FNN algorithm. - A jupyter notebook called Random_Forest.ipynb which was used to generate the results for the Random Forest algorithm. - A jupyter notebook called XGBoost.ipynb which was used to generate the results for the XGBoost algorithm.Item Open Access Code and Data: Multimodal Approach for Pilot Mental State Detection Based on EEG(Cranfield University, 2023-08-23 15:04) Alreshidi, Ibrahim; Moulitsas, Irene; Jenkins, KarlData: This folder contains: A dataset called Crews_equalized_dataset_epo.fif which was used to obtain the results presented in the journal paper. It is the preprocessed EEG dataset used to predict four mental states, Channelised Attention, Diverted Attention, Startle/Surprise, and Baseline. A dataset called Example_raw.fif which was used to obtain Figure 6 of the journal paper. Source code: This folder contains a jupyter notebook called python_code.ipynb which implements the proposed EEG preprocessing pipeline and all the algorithms presented and validated in the journal paper. Output: This folder contains: A figure called Confusion Matrices.jpg which shows results from the Random Forest classifier in (A), Extremely Randomized Trees in (B), Gradient Tree Boosting in (C), AdaBoost in (D), and Voting in (E). Figures called Figure 6A.jpg and Figure 6B.jpg which show the EEG signals before applying the preprocessing pipeline, and after applying the preprocessing pipeline, respectively. A text file called ML models evaluation.txt which contains the results produced by all algorithms presented and validated in the journal paper. A figure called The preprocessed EEG signals.jpg which shows the EEG signals, upon completion of our preprocessing pipeline, fed into the machine learning models for training and testing purposes.Item Open Access CODE_MRF.zip(Cranfield University, 2023-10-11 09:00) Silva, Paulo; Tsoutsanis, Panagiotissource codeItem Open Access Data supporting: 'Hybrid Terrain Traversability Analysis in Off-road Environments'(Cranfield University, 2022-09-05 10:37) Leung, TigaCitation: Leung THY, Ignatyev D, Zolotas A. (2022) Hybrid terrain traversability analysis in off-road environments. In: 2022 8th International Conference on Automation, Robotics and Applications (ICARA), 18 February - 20 March 2022, Prague, Czech RepublicAbstract: There is a significant growth in autonomy level in off-road ground vehicles. However, unknown off-road environments are often challenging due to their unstructured and rough nature. To find a path that the robot can move smoothly to its destination, it needs to analyse the surrounding terrain. In this paper, we present a hybrid terrain traversability analysis framework. Semantic segmentation is implemented to understand different types of the terrain surrounding the robot; meanwhile geometrical properties of the terrain are assessed with the aid of a probabilistic terrain estimation. The framework represents the traversability analysis on a robot-centric cost map, which is available to the path planners. We evaluated the proposed framework with synchronised sensor data captured while driving the robot in real off-road environments. This thorough terrain traversability analysis will be crucial for autonomous navigation systems in off-road environments.Item Open Access Extremum Seeking Control for Truck Drag Reduction(Cranfield University, 2018-06-20 16:09) Whidborne, James; Garry, KevinMATLAB/Simulink codes for "Extremum Seeking Control for Truck Drag Reduction" G Papageorgiou, J Barden, J Whidborne, K Garry 12th UKACC International Conference on Control Sheffield, UK, 5th - 7th September 2018 Videos: converge.mp4 - shows ESC controller convergence with Speed controller reference, Vr=24 m/s Initial deflector height limits, deltaH=1.16 Gradient and wind speed are set to zeroItem Open Access MATLAB code of examples of solving optimal control problems using the Chebfun system(Cranfield University, 2022-10-03 15:44) Whidborne, JamesMATLAB code of examples of solving optimal control problems using the Chebfun system. liftdragpolar.m - Lift-Drag Polar Example kaiserdiagram.m - Minimum Time to Climb (Kaiser diagram) intercept.m - Intercept Problem trajplanner.m - Minimum Time Trajectory Planning Problem Requires the Chebfun system Please acknowledge and reference via: J.F. Whidborne. Solving optimal control problems using the Chebfun systeml, UKACC Control 2016, Befast, U.K. September 2016. (doi:10.1109/CONTROL.2016.7737522)Item Open Access Prediction of Flight Delay using Deep Operator Network with Gradient-mayfly Optimisation Algorithm(Cranfield University, 2024-02-01 08:47) Bala Bisandu, Desmond; Moulitsas, IreneData: This folder contains: - Datasets called Jan_2021_ontime.csv and Nov_2021_ontime.csv were used to obtain the results presented in the journal paper. Source code: This folder contains two files having the instructions on how to run the code and a list of library requirements and folders for each of the ML models as named exactly as contained in the paper which implements the proposed Deep Operator Network with Gradient-mayfly Optimisation Algorithm and all the algorithms presented and validated in the journal paper. Output: This folder contains: - Figures called Figure_1_MAE.png, Figure_1_MAPE.png, Figure_1_RMSE.png, Figure_1_MSE.png, Figure_2_MAE.png,Figure_2_MAPE.png, Figure_2_RMSE.png, Figure_2_MSE.png which shows results from the models based on different train/test ratios, The models are: (A)DBN, (B) Gradient Boosting Classifier, (C) Information Gain-SVM, (D) Multi-Agent Approach, (E) DeepLSTM, (F) SSDCA-based Deep LSTM, (G) DeepONet and (H) Proposed GMOA-based DeepOnet.- Figures called Figure 6A.jpg and Figure 6B.jpg which show the EEG signals before applying the preprocessing pipeline, and after applying the preprocessing pipeline, respectively. - Figures called Figure_1_Prediction_Result_Jan_2021.png and Figure_2_Prediction_Result_Nov_2021.png which are the plots of the prediction results from presented in the journal paper. - 8 csv files called 1 MAE.csv, 1 MAPE.csv, 1 RMSE.csv, 1 MSE.csv, 2 MAE.csv, 2 MAPE.csv, 2 RMSE.csv, 2 MSE.csv, which contains the evaluation results produced by all algorithms presented and validated in the journal paper. - 2 csv files called Delay Prediction_Jan_2021_ontime_Figure_1 and Delay Prediction_Nov_2021_ontime_Figure_2 which contains the prediction results produced by all algorithms presented and validated in the journal paper.Item Open Access Repository for "Automatic Sentiment Lexicon Creation for Airport Services Reviews Using Pointwise Mutual Information"(Cranfield University, 2023-12-11 16:09) Salih A Homaid, Mohammed; Moulitsas, Irene; Chandrakumar, MathuraIn this study, we propose a novel method to generate domain-specific sentiment lexicons for airport service reviews utilising the VADER sentiment lexicon dictionary. First, we scraped the data from the SKYTRAX website, which is a collection of reviews of around 600 airports. Then, data pre-processing techniques were employed including unigrams extraction and stopwords removal. Having done that, we employed pointwise mutual information to calculate the scores of the extracted unigrams. Then, we updated the default scores of VADER with the pointwise mutual information scores. We evaluated our results using the performance measures of accuracy, precision, recall, and F1-score. Two popular general sentiment lexicons are used as benchmarks. The results showed that our proposed lexicon dictionary for the domain of airport reviews outperformed the benchmarks with consistent considerable improvements achieving around 10% in accuracy and around 7% in F1-score.Item Open Access Simulink/MATLAB code of simulation of L-1 adaptive control for a quadrotor subject to actuator faults(Cranfield University, 2022-10-03 15:56) Whidborne, JamesSimulink/MATLAB code of simulation of L-1 adaptive control for a quadrotor subject to actuator faults. QuadrotorL1initialize.m - Initialize Simulink code for simulation of L_1 adaptive control of a quadrotor QuadrotorL1adaptive.slx - Simulink code for simulation of L_1 adaptive control of a quadrotor QuadrotorLQRIinitialize.m - Initialize Simulink code for simulation of baseline LQR controller + integral action for a quadrotor QuadrotorLQRI.slx - Simulink code for simulation of baseline LQR controller + integral action for a quadrotor Additional codes can be downloaded from l1-adaptive-control-tutorials Please acknowledge and reference via: D. Xu, J.F. Whidborne and A. Cooke. Fault tolerant control of a quadrotor using L-1 adaptive control. International Journal of Intelligent Unmanned Systems.4(1):43-66, 2016. (doi:10.1108/IJIUS-08-2015-0011)Item Open Access Supporting code and data for 'Miscellaneous EEG Preprocessing and Machine Learning for Pilots' Mental States Classification: Implications'(Cranfield University, 2023-09-18 16:10) Alreshidi, Ibrahim; Moulitsas, Irene; Jenkins, KarlData: This folder contains: - A dataset called Pilot_5_CA_raw.fif, which contains the EEG data of a pilot when he was experiencing the channelised attention state in a non-flight environment. - A dataset called Pilot_5_DA_raw.fif, which contains the EEG data of a pilot when he was experiencing the diverted attention state in a non-flight environment. - A dataset called Pilot_5_SS_raw.fif, which contains the EEG data of a pilot when he was experiencing the startle/surprise state in a non-flight environment. - A dataset called Pilot_5_LOFT_raw.fif, which contains the EEG data of a pilot when he was experiencing the channelised attention, diverted attention, and startle/surprise state in a flight simulator environment. Source code: This folder contains: - A jupyter notebook called ICAAI_Conference.ipynb which was used to generate the results of the study. - A python file called cf_matrix which was used to plot the confusion matrixItem Open Access Supporting data and code for 'Illuminating the Neural Landscape of Pilot Mental States: A Convolutional Neural Network Approach with SHAP Interpretability'(Cranfield University, 2024-01-08 16:35) Alreshidi, Ibrahim; Moulitsas, Irene; Bisandu, DesmondData: This folder contains: - PSD (Power Spectral Density) features and labels datasets for individual pilots. These were leveraged to acquire the results presented in Table 2 of our article. For results pertaining to a specific pilot, two files are utilised to train our proposed model: "Pilot_i_EEG_band_power_features.npy" and "Pilot_i_events.npy". In these filenames, 'i' represents the pilot's unique ID number. The file "Pilot_i_EEG_band_power_features.npy" contains power spectral density features extracted from five distinct frequency bands: delta, theta, alpha, beta, and gamma. On the other hand, "Pilot_i_events.npy" contains the class labels indicating the mental state of the pilot: 0 for baseline, 1 for startle/surprise, 2 for channelized attention, and 3 for diverted attention. - A combined dataset named "EEG_band_power_features.npy", which comprises the PSD features for all pilots. Its corresponding class labels are found in the "all_events.npy" file. This combined dataset was instrumental in deriving the results published in our paper. Source code: This folder contains: - A jupyter notebook called EEG_Stats.ipynb which computes the PSD features using the original EEG data for each pilot. It also include the source code to compute the average power in each frequency band for each mental state and the average power in each frequency band for each EEG channel using the combined pilots dataset. - A jupyter notebook called Ind_pilot_conv_model.ipynb which implements the proposed 1D-CNN approach presented and tested in the journal paper for each pilot. - A jupyter notebook called all_pilots.ipynb which implements the proposed 1D-CNN approach presented and tested in the journal paper for all pilots. It also includes the source code to obtain the training accuracy and loss curves, compute the confusion matrix, and obtain the top 10 important features for each mental state. Output: This folder contains: - A figure called "The average power in each frequency band across pilots" which shows the average power in each frequency band for each mental state using the combined pilots dataset. - A figure called "Heatmap for the average power in each frequency band for EEG channels" which shows the average power in each frequency band for each EEG channel using the combined pilots dataset. - A figure called "Confusion Matrix" which shows the confusion matrix results of the proposed 1D-CNN model using the combined pilots dataset. - A figure called "Accuracy and loss curve" which shows the training accuracy and loss curves results of the proposed 1D-CNN model using the combined pilots dataset. - A figure called "Top 10 important features for NE class" which shows the top 10 important features for detecting the baseline state using the combined pilots dataset. - A figure called "Top 10 important features for SS class" which shows the top 10 important features for detecting the Startle/Surprise state using the combined pilots dataset. - A figure called "Top 10 important features for CA class" which shows the top 10 important features for detecting the Channelised Attention state using the combined pilots dataset. - A figure called "Top 10 important features for DA class" which shows the top 10 important features for detecting the Diverted Attention state using the combined pilots dataset. - A text file called "1D-CNN model evaluation" which contain the results produced by all the proposed 1D-CNN model presented and tested in the journal paper.Item Open Access Taxi Time and Energy Analysis Python Program(Cranfield University, 2023-08-21 09:24) Hin Pang, ChiThis program runs with Python. With at least one File List, the program is able to execute and calculate the taxi time and taxi energy requirement for users.Item Open Access The Effect of Rotor Tilt on the Stability and Gust Rejection Properties of VTOL Multirotor Aircraft - MATLAB Codes(Cranfield University, 2022-01-04 11:11) Whidborne, JamesVideo visualization and MATLAB files and routines to generate figures, video and some results of paper "The Effect of Rotor Tilt on the Stability and Gust Rejection Properties of VTOL Multirotor Aircraft" by James F. Whidborne, Arthur Mendez and Alastair CookeRun the script MultiRotorGust.m to generate all the figures & the video in the paper.