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Cranfield is an exclusively postgraduate university that is a global leader in education and transformational research in technology and management. We are focused on the specialist themes of aerospace, defence and security, energy and power, environment and agrifood, manufacturing, transport systems, and water. The Cranfield School of Management is a world leader in management education and research. We are home to many world-class, large-scale facilities which enhance our teaching and research.
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Browsing CORD - Cranfield University research data by Type "Software"
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Item Open Access A Design Approach to IoT Endpoint Security for Production Machinery Monitoring(Cranfield University, 2019-05-22 16:09) Tedeschi, Stefano; Emmanouilidis, Christos; Mehnen, Jörn; Roy, RajkumarThe Internet of Things (IoT) has significant potential in upgrading legacy production machinery with monitoring capabilities to unlock new capabilities and bring economic benefits. However, the introduction of IoT at the shop floor layer exposes it to additional security risks with potentially significant adverse operational impact. This project addresses such fundamental new risks at their root by introducing a novel endpoint security by design approach. The approach is implemented on a widely applicable production machinery monitoring application by introducing real time adaptation features for IoT device security through subsystem isolation and a dedicated lightweight authentication protocol.Item Open Access A Novel Hybrid Electrochemical Equivalent Circuit Model for Online Battery Management Systems(Cranfield University, 2024-08-02) Cai, Chengxi; Auger, DanielA Novel Hybrid Electrochemical Equivalent Circuit Model for Online Battery Management SystemsItem 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 Aerosonde Simulation(Cranfield University, 2018-03-15 14:53) Pelham, JoniAerosonde Simulink simulation with mission simulator, and fault simulator. Development of work by Whidborne, Saban, et alItem 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 Control of plane Poiseuille flow: a theoretical and computational investigation - MATLAB code(Cranfield University, 2022-10-03 15:31) Whidborne, James; McKernan, JohnFeedback Control of Plane Poiseuille Flow - MATLAB Codemincode-version2.zip File contains the functions for the system matrices A,B,C and energy matrix Q for modeling linearized plane Poiseuille flow with wall-transpiration actuation and wall shear-stress measurements, which may be of use for controller synthesis. See mincode.m to begin. Please acknowledge and reference via thesis: Mckernan, J. Control of plane Poiseuille flow: a theoretical and computational investigation, PhD Thesis, School of Engineering, Cranfield University, 2006Item Open Access DailyET(Cranfield University, 2019-06-04 12:19) Hess, TimA simple on-screen calculator to calculate daily, or monthly reference evapotranspiration from weather data according to the Penman, Modified Penman (FAO24), Penman Monteith (FAO56) or Hargreaves methods.Item 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 Data underpinning: 'NERC Research Translation: Grassland Management' project(Cranfield University, 2022-09-08 13:38) Giannitsopoulos, Michail; Burgess, Paul; Richter, Goetz; Bell, Matthew; F. E. Topp, Cairistiona; Ingram, Julie; Takahashi, TaroLINGRA-N-Plus along with its Teaching Guide, as developed in the NERC Research Translation: Grassland Management Project, supported by the Sustainable Agriculture Research and Innovation Club (SARIC).Item Open Access dsmcFoam+ case setup for the Orion Crew Module(Cranfield University, 2021-06-04 09:10) Teschner, Tom-RobinThis material provides the OpenFOAM case setup files for the Orion Crew Module (OCM) in support of the submitted manuscript "Aerodynamic performance investigation through different chemistry modelling approaches for space re-entry vehicles using the DSMC method", presented at the UKACM 2022 conference. The case setup files are for the dsmcFoam+ solver and provide the mesh as well as the full setup for the Orion Crew Module (OCM) in 2D using the QK chemical model. All reactions are implemented for a 5-species flow.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 Farm-SAFE v3 - Comparing the financial benefits and costs of arable, forest, and agroforestry systems(Cranfield University, 2024-02-06 13:58) Graves, Anil; Burgess, Paul; Wiltshire, Katy; Giannitsopoulos, Michail; Herzog, Felix; Palma, JoaoAgroforestry systems integrate trees with livestock and/or arable crops on the same parcel of land. Compared to monoculture arable or grass systems, agroforestry systems can enhance soil conservation, carbon sequestration, species and habitat diversity, and provide additional sources of farm income. Farm-SAFE (Financial and Resource use Model for Simulating AgroForestry in Europe) is a spreadsheet-based bio-economic model which has been developed in Microsoft® Excel® to compare the financial benefits and costs of crop-only, tree-only, and agroforestry system over tree rotations of up to 60 years (Graves et al., 2024a). The results are presented in both graphical and tabular form in terms of a net present value and equivalent annual values. A description and user guide is also available (Graves et al., 2024b). Farm-SAFE requires input of tree and crop yields. One way to obtain crop and tree yields in tree-only, agroforestry, and crop-only systems is to use the Yield-SAFE model. Yield-SAFE is a spreadsheet-based biophysical model which has been developed to enable the prediction of the relationship between tree and crop yields over the rotation of the tree component. A copy of the Yield-SAFE model, together with a full description and user guide, is available here. The original Farm-SAFE model was developed with funding from the European Union through the Silvoarable Agroforestry For Europe project (contract number QLK5-CT-2001-00560). The process of creating a default publicly available version of the model has been enabled through the BioForce project funded by the UK Department for Energy Security and Net Zero. Graves, A.R., Burgess, P.J., Wiltshire, C., Giannitsopoulos, M., Herzog, F., Palma, J.H.N. (2024a). Farm-SAFE v3 model in Excel. Cranfield, Bedfordshire, UK: Cranfield University. Graves, A.R., Burgess, P.J., Wiltshire, C., Giannitsopoulos, M., Herzog, F., Palma, J.H.N. (2024b). Description and User Guide for Farm-SAFE v3. January 2024. Cranfield, Bedfordshire, UK: Cranfield University. 42 pp.Item Open Access FFT of square wave generation script(Cranfield University, 2018-01-29 08:55) Barrington, James; James, StephenPython 2.7 script used to create a discrete FFT of a square wave associated with our paper 'The effect of UV irradiation duty cycle on the transmission spectra of optical fiber long period gratings'Item Open Access GBSAR-Proc(Cranfield University, 2019-08-22 15:56) Elgy, JamesThe GBSAR-Proc Python package is designed to load and process data gathered from Cranfield University's ground based Synthetic Aperture Radar (SAR) system. Included in the package are a series of classes designed to manipulate raw data, process it into range profiles and finally use the Backprojection Algorithm to plot high quality near-field SAR images. In addition to the more common planar SAR images, there is functionality to both generate and plot volumetric SAR images, formed on either the CPU or GPU.Item 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 'P. Machuca - Ph.D. Code Repository'(Cranfield University, 2021-03-13 10:08) Antonio Machuca Varela, PabloThis reposity contains examples of simulations contained in P. Machuca's Ph.D. thesis (Cranfield University, 2020):"MISSION DESIGN AND AUTONOMOUS DEEP-SPACE OPERATIONS FOR THE LOW-COST EXPLORATION OF ASTEROIDS USING CUBESATS"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.