A deep reinforcement learning based homeostatic system for unmanned position control
Conference item
Manning, Warren, Anjum, Ashiq, Bower, Craig and Dassanayake, Priyanthi 2019. A deep reinforcement learning based homeostatic system for unmanned position control. Association for Computing Machinery.
| Authors | Manning, Warren, Anjum, Ashiq, Bower, Craig and Dassanayake, Priyanthi |
|---|---|
| Abstract | Deep Reinforcement Learning (DRL) has been proven to be capable of designing an optimal control theory by minimising the error in dynamic systems. However, in many of the real-world operations, the exact behaviour of the environment is unknown. In such environments, random changes cause the system to reach different states for the same action. Hence, application of DRL for unpredictable environments is difficult as the states of the world cannot be known for non-stationary transition and reward functions. In this paper, a mechanism to encapsulate the randomness of the environment is suggested using a novel bio-inspired homeostatic approach based on a hybrid of Receptor Density Algorithm (an artificial immune system based anomaly detection application) and a Plastic Spiking Neuronal model. DRL is then introduced to run in conjunction with the above hybrid model. The system is tested on a vehicle to autonomously re-position in an unpredictable environment. Our results show that the DRL based process control raised the accuracy of the hybrid model by 32%. |
| Keywords | Deep Learning; Deep Reinforcement Learning; Artificial Immune System |
| Year | 2019 |
| Journal | Proceedings of the 6th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies - BDCAT '19 |
| Publisher | Association for Computing Machinery |
| Web address (URL) | http://hdl.handle.net/10545/624551 |
| http://www.acm.org/publications/policies/copyright_policy#Background | |
| hdl:10545/624551 | |
| ISBN | 9781450370165 |
| File | File Access Level Open |
| File | File Access Level Open |
| Publication dates | 05 Dec 2019 |
| Publication process dates | |
| Deposited | 05 Mar 2020, 15:35 |
| Accepted | 16 Oct 2019 |
| Contributors | University of Derby |
Permalink -
https://repository.derby.ac.uk/item/94z6q/a-deep-reinforcement-learning-based-homeostatic-system-for-unmanned-position-control
Download files
File
| A deep reinforcement learning based homeostatic system.pdf | ||
| File access level: Open | ||
 | license.txt | |
| File access level: Open | ||
115
total views27
total downloads4
views this month0
downloads this month
Export as
Related outputs
A Robust Internet of Drones Security Surveillance Communication Network Based on IOTA
Gilani, S. Y., Anjum, A., Khan, A., Khan, A., Syed, M. H., Moqurrab, S. A. and Srivastava, G. 2024. A Robust Internet of Drones Security Surveillance Communication Network Based on IOTA. Internet of Things. pp. 1-21. https://doi.org/10.1016/j.iot.2024.101066A Secure and Privacy Preserved Infrastructure for VANETs based on Federated Learning with Local Differential Privacy
Batool, H., Anjum, A., Khan, A., Izzo, S., Mazzocca, C. and Jeon, G. 2023. A Secure and Privacy Preserved Infrastructure for VANETs based on Federated Learning with Local Differential Privacy. Elsevier Information Sciences. 652. https://doi.org/10.1016/j.ins.2023.119717Cohort-based kernel principal component analysis with Multi-path Service Routing in Federated Learning
Sikandar, H. S., Malik, S. R., Anjum, A., Khan, A. and Jeon, G. 2023. Cohort-based kernel principal component analysis with Multi-path Service Routing in Federated Learning. Future Generation Computer Systems. 149, pp. 518-530. https://doi.org/10.1016/j.future.2023.07.037An Efficient and Privacy-preserving Blockchain-based Secure Data Aggregation in Smart Grids
Mahmood, A., Khan, A., Anjum, A., Maple, C. and Jeon, G. 2023. An Efficient and Privacy-preserving Blockchain-based Secure Data Aggregation in Smart Grids. Sustainable Energy Technologies and Assessments. 60, pp. 1-11. https://doi.org/10.1016/j.seta.2023.103414Privacy Preservation in the Internet of Vehicles using Local Differential Privacy and IOTA Ledger
Iftikhar, Z., Anjum, A., Jeon, G., Shah, M. A. and Khan, A. 2023. Privacy Preservation in the Internet of Vehicles using Local Differential Privacy and IOTA Ledger. Springer Cluster Computing . pp. 1-17. https://doi.org/10.1007/s10586-023-04002-0Preserving Privacy of High-Dimensional Data by l-Diverse Constrained Slicing
Amin, Z., Anjum, A., Khan, A., Ahmad, A. and Jeon, G. 2022. Preserving Privacy of High-Dimensional Data by l-Diverse Constrained Slicing. Electronics. 11 (8), p. 1257. https://doi.org/10.3390/electronics11081257Fuzz-classification (p, l)-Angel: An enhanced hybrid artificial intelligence based fuzzy logic for multiple sensitive attributes against privacy breaches
Kanwal, T., Attaullaha, H., Anjum, A., Khan, A. and Jeon, G. 2022. Fuzz-classification (p, l)-Angel: An enhanced hybrid artificial intelligence based fuzzy logic for multiple sensitive attributes against privacy breaches. Elsevier Digital Communications and Networks. pp. 1-16. https://doi.org/10.1016/j.dcan.2022.09.025Explaining deep neural networks: A survey on the global interpretation methods
Saleem, R., Yuan, B., Kurugollu, F., Anjum, A. and Liu, L. 2022. Explaining deep neural networks: A survey on the global interpretation methods. Neurocomputing. 513, pp. 165-180. https://doi.org/10.1016/j.neucom.2022.09.129A unified graph model based on molecular data binning for disease subtyping
Hassan Zada, M., Yuan, B, Khan, W., Anjum, A., Reiff-Marganiec, S. and Saleem, R. 2022. A unified graph model based on molecular data binning for disease subtyping. Journal of Biomedical Informatics. pp. 1-24. https://doi.org/10.1016/j.jbi.2022.104187Large-scale Data Integration Using Graph Probabilistic Dependencies (GPDs)
Zada, Muhammad Sadiq Hassan, Yuan, Bo, Anjum, Ashiq, Azad, Muhammad Ajmal, Khan, Wajahat Ali and Reiff-Marganiec, Stephan 2020. Large-scale Data Integration Using Graph Probabilistic Dependencies (GPDs). IEEE. https://doi.org/10.1109/bdcat50828.2020.00028Explaining probabilistic Artificial Intelligence (AI) models by discretizing Deep Neural Networks
Saleem, Rabia, Yuan, Bo, Kurugollu, Fatih and Anjum, Ashiq 2020. Explaining probabilistic Artificial Intelligence (AI) models by discretizing Deep Neural Networks. IEEE. https://doi.org/10.1109/ucc48980.2020.00070Embedded Data Imputation for Environmental Intelligent Sensing: A Case Study
Erhan, Laura, Di Mauro, Mario, Anjum, Ashiq, Bagdasar, Ovidiu, Song, Wei and Liotta, Antonio 2021. Embedded Data Imputation for Environmental Intelligent Sensing: A Case Study. Sensors. 21 (23), p. 7774. https://doi.org/10.3390/s21237774Intelligent price alert system for digital assets - cryptocurrencies
Chhem, Sronglong, Anjum, Ashiq and Arshad, Bilal 2019. Intelligent price alert system for digital assets - cryptocurrencies. ACM Press. https://doi.org/10.1145/3368235.3368874Congestion prediction for smart sustainable cities using IoT and machine learning approaches
Majumdar, Sharmila, Subhani, Moeez M., Roullier, Benjamin, Anjum, Ashiq and Zhu, Rongbo 2020. Congestion prediction for smart sustainable cities using IoT and machine learning approaches. Sustainable Cities and Society. 64, p. 102500. https://doi.org/10.1016/j.scs.2020.102500Intelligent data fusion algorithm based on hybrid delay-aware adaptive clustering in wireless sensor networks
Liu, Xiaozhu, Zhu, Rongbo, Anjum, Ashiq, Wang, Jun, Zhang, Hao and Ma, Maode 2019. Intelligent data fusion algorithm based on hybrid delay-aware adaptive clustering in wireless sensor networks. Future Generation Computer Systems. 104, pp. 1-14. https://doi.org/10.1016/j.future.2019.10.001Fog computing-based approximate spatial keyword queries with numeric attributes in IoV
Li, Yanhong, Zhu, Rongbo, Mao, Shiwen and Anjum, Ashiq 2020. Fog computing-based approximate spatial keyword queries with numeric attributes in IoV. IEEE Internet of Things. https://doi.org/10.1109/jiot.2020.2965730A cascade learning approach for automated detection of locomotive speed sensor using imbalanced data in ITS
Li, Bo, Zhou, Sisi, Cheng, Lifang, Zhu, Rongbo, Hu, Tao, Anjum, Ashiq, He, Zheng and Zou, Yongkai 2019. A cascade learning approach for automated detection of locomotive speed sensor using imbalanced data in ITS. IEEE Access. 7, pp. 90851-90862. https://doi.org/10.1109/access.2019.2928224Adversarial Thresholding Semi-Bandits
Bower, Craig 2020. Adversarial Thresholding Semi-Bandits. ThesisExploring network embedding for efficient message routing in opportunistic mobile social networks
Yuan, Bo, Anjum, Ashiq, Panneerselvam, J. and Liu, Lu 2020. Exploring network embedding for efficient message routing in opportunistic mobile social networks. IEEE. https://doi.org/10.1109/ICDMW.2019.00077Graph data modelling for genomic variants
Anjum, Ashiq and Aizad, Sanna 2019. Graph data modelling for genomic variants.Multiclass disease predictions based on integrated clinical and genomics datasets
Anjum, Ashiq and Subhani, Moeez 2019. Multiclass disease predictions based on integrated clinical and genomics datasets. IARIA.Improved Kalman filter based differentially private streaming data release in cognitive computing.
Wang, Jun, Luo, Jing, Liu, Xiaozhu, Li, Yongkai, Liu, Shubo, Zhu, Rongbo and Anjum, Ashiq 2019. Improved Kalman filter based differentially private streaming data release in cognitive computing. Future Generation Computer Systems. https://doi.org/10.1016/j.future.2019.03.050Intelligent augmented keyword search on spatial entities in real-life internet of vehicles
Li, Yanhong, Wang, Meng, Du, Xiaokun, Feng, Yuhe, Luo, Changyin, Tian, Shasha, Anjum, Ashiq and Zhu, Rongbo 2018. Intelligent augmented keyword search on spatial entities in real-life internet of vehicles. Future Generation Computer Systems. 94, pp. 697-711. https://doi.org/10.1016/j.future.2018.12.051Language model-based automatic prefix abbreviation expansion method for biomedical big data analysis
Anjum, Ashiq 2019. Language model-based automatic prefix abbreviation expansion method for biomedical big data analysis. Future Generation Computer Systems. https://doi.org/10.1016/j.future.2019.01.016Machine-learning-based side-channel evaluation of elliptic-curve cryptographic FPGA processor.
Mukhtar, Naila, Mehrabi, Mohamad, Kong, Yinan and Anjum, Ashiq 2018. Machine-learning-based side-channel evaluation of elliptic-curve cryptographic FPGA processor. Applied Sciences. 9 (1), p. 64. https://doi.org/10.3390/app9010064Providing traceability for neuroimaging analyses.
McClatchey, Richard, Branson, Andrew, Anjum, Ashiq, Bloodsworth, Peter, Habib, Irfan, Munir, Kamran, Shamdasani, Jetendr and Soomro, Kamran 2013. Providing traceability for neuroimaging analyses. International Journal of Medical Informatics.CMS workflow execution using intelligent job scheduling and data access strategies.
Hasham, Khawar, Delgado Peris, Antonio, Anjum, Ashiq, Evans, Dave, Gowdy, Stephen, Hernandez, José M., Huedo, Eduardo, Hufnagel, Dirk, van Lingen, Frank, McClatchey, Richard and Metson, Simon 2011. CMS workflow execution using intelligent job scheduling and data access strategies. IEEE Transactions on Nuclear Science. https://doi.org/10.1109/TNS.2011.2146276Context-aware service utilisation in the clouds and energy conservation.
Kiani, Saad Liaquat, Anjum, Ashiq, Antonopoulos, Nick and Knappmeyer, Michael 2012. Context-aware service utilisation in the clouds and energy conservation. Journal of Ambient Intelligence and Humanized Computing. https://doi.org/10.1007/s12652-012-0131-1Exploiting in-memory systems for gnomic data analysis.
Shah, Zeeshan Ali, El-Kalioby, Mohamed, Faquih, Tariq, Shokrof, Moustafa, Subhani, Shazia, Alnakhli, Yasser, Aljafar, Hussain, Anjum, Ashiq and Abouelhoda, Mohamed 2018. Exploiting in-memory systems for gnomic data analysis. Springer. https://doi.org/10.1007/978-3-319-78723-7_35Cloud-based video analytics using convolutional neural networks.
Yaseen, M., Anjum, Ashiq, Farid, Mohsen and Antonopoulos, Nick 2018. Cloud-based video analytics using convolutional neural networks. Software Practice and Experience. https://doi.org/10.1002/spe.2636Deep learning hyper-parameter optimization for video analytics in clouds.
Yaseen, M., Anjum, Ashiq, Rana, Omer and Antonopoulos, Nikolaos 2018. Deep learning hyper-parameter optimization for video analytics in clouds. IEEE Transactions on Systems, Man, and Cybernetics. https://doi.org/10.1109/TSMC.2018.2840341Edge enhanced deep learning system for large-scale video stream analytics.
Muhammad, A., Anjum, Ashiq, Yaseen, M. Usman, Zamani, A. Reza, Balouek-Thomert, Daniel, Rana, Omer and Parashar, Manish 2018. Edge enhanced deep learning system for large-scale video stream analytics. IEEE. https://doi.org/10.1109/CFEC.2018.8358733Representing variant calling format as directed acyclic graphs to enable the use of cloud computing for efficient and cost effective genome analysis
Aizad, Sanna, Anjum, Ashiq and Sakellariou, Rizos 2017. Representing variant calling format as directed acyclic graphs to enable the use of cloud computing for efficient and cost effective genome analysis. IEEE. https://doi.org/10.1109/CCGRID.2017.116Blockchain standards for compliance and trust
Anjum, Ashiq, Sporny, Manu and Sill, Alan 2017. Blockchain standards for compliance and trust. IEEE Cloud Computing. https://doi.org/10.1109/MCC.2017.3791019Big data analytics in healthcare: A cloud based framework for generating insights
Anjum, Ashiq, Aizad, Sanna, Arshad, Bilal, Subhani, Moeez, Davies-Tagg, Dominic, Abdullah, Tariq and Antonopoulos, Nikolaos 2017. Big data analytics in healthcare: A cloud based framework for generating insights. in: Springer.Clinical and genomics data integration using meta-dimensional approach
Subhani, Moeez, Anjum, Ashiq, Koop, Andreas and Antonopoulos, Nikolaos 2016. Clinical and genomics data integration using meta-dimensional approach. Association for Computing Machinery. https://doi.org/10.1145/2996890.3007896Deadline constrained video analysis via in-transit computational environments
Zamani, Ali Reza, Zou, Mengsong, Diaz-Montes, Javier, Petri, Ioan, Rana, Omer, Anjum, Ashiq and Parashar, Manish 2017. Deadline constrained video analysis via in-transit computational environments. IEEE Transactions on Services Computing. https://doi.org/10.1109/TSC.2017.2653116Data Intensive and Network Aware (DIANA) grid scheduling
McClatchey, Richard, Anjum, Ashiq, Stockinger, Heinz, Ali, Arshad, Willers, Ian and Thomas, Michael 2007. Data Intensive and Network Aware (DIANA) grid scheduling. Journal of Grid Computing. https://doi.org/10.1007/s10723-006-9059-zIntelligent grid enabled services for neuroimaging analysis
McClatchey, Richard, Habib, Irfan, Anjum, Ashiq, Munir, Kamran, Branson, Andrew, Bloodsworth, Peter and Kiani, Saad Liaquat 2013. Intelligent grid enabled services for neuroimaging analysis. Neurocomputing. https://doi.org/10.1016/j.neucom.2013.01.042Cloud-based scalable object detection and classification in video streams
Yaseen, M., Anjum, Ashiq, Rana, Omer and Hill, Richard 2017. Cloud-based scalable object detection and classification in video streams. Future Generation Computer Systems. https://doi.org/10.1016/j.future.2017.02.003Modeling and analysis of a deep learning pipeline for cloud based video analytics.
Yaseen, M., Anjum, Ashiq and Antonopoulos, Nikolaos 2017. Modeling and analysis of a deep learning pipeline for cloud based video analytics. https://doi.org/10.1145/3148055.3148081Traffic monitoring using video analytics in clouds
Abdullah, Tariq, Anjum, Ashiq, Tariq, M. Fahim, Baltaci, Yusuf and Antonopoulos, Nikolaos 2014. Traffic monitoring using video analytics in clouds. IEEE. https://doi.org/10.1109/UCC.2014.12Big-Data analytics and cloud computing: Theory, algorithms and applications
Hill, Richard, Trovati, Marcello, Liu, Lu, Anjum, Ashiq and Zhu, Shao Ying 2015. Big-Data analytics and cloud computing: Theory, algorithms and applications. Springer.A cloud resource management model for the creation and orchestration of social communities
Ikram, Ahsan, Anjum, Ashiq and Bessis, Nik 2015. A cloud resource management model for the creation and orchestration of social communities. Simulation Modelling Practice and Theory. https://doi.org/10.1016/j.simpat.2014.05.003Federated broker system for pervasive context provisioning
Kiani, Saad Liaquat, Anjum, Ashiq, Knappmeyer, Michael, Bessis, Nik and Antonopoulos, Nikolaos 2013. Federated broker system for pervasive context provisioning. Journal of Systems and Software. https://doi.org/10.1016/j.jss.2012.11.050Adapting scientific workflow structures using multi-objective optimization strategies
Habib, Irfan, Anjum, Ashiq, Mcclatchey, Richard and Rana, Omer 2013. Adapting scientific workflow structures using multi-objective optimization strategies. ACM Transactions on Autonomous and Adaptive Systems. https://doi.org/10.1145/2451248.2451252Video stream analysis in clouds: An object detection and classification framework for high performance video analytics
Anjum, Ashiq, Abdullah, Tariq, Tariq, M. Fahim, Baltaci, Yusuf and Antonopoulos, Nikolaos 2016. Video stream analysis in clouds: An object detection and classification framework for high performance video analytics. IEEE Transactions on Cloud Computing. https://doi.org/10.1109/TCC.2016.2517653Towards cloud based big data analytics for smart future cities
Khan, Zaheer, Anjum, Ashiq, Tahir, Muhammad Atif and Soomro, Kamran Ahmed 2015. Towards cloud based big data analytics for smart future cities. Journal of Cloud Computing. https://doi.org/10.1186/s13677-015-0026-8Spatial frequency based video stream analysis for object classification and recognition in clouds
Yaseen, M., Anjum, Ashiq and Antonopoulos, Nikolaos 2016. Spatial frequency based video stream analysis for object classification and recognition in clouds. IEEE.High performance video processing in cloud data centres
Yaseen, M., Zafar, Muhammad Sarim, Anjum, Ashiq and Hill, Richard 2016. High performance video processing in cloud data centres. IEEE. https://doi.org/10.1109/SOSE.2016.56An Inter-Cloud Meta-Scheduling (ICMS) simulation framework: architecture and evaluation
Sotiriadis, Stelios, Bessis, Nik, Anjum, Ashiq and Buyya, Rajkumar 2015. An Inter-Cloud Meta-Scheduling (ICMS) simulation framework: architecture and evaluation. IEEE Transactions on Services Computing. https://doi.org/10.1109/TSC.2015.2399312On optimization of surface roughness of selective laser melted stainless steel parts: A statistical study
Alrbaey, K., Wimpenny, D., Tosi, R., Manning, W. and Moroz, A. 2014. On optimization of surface roughness of selective laser melted stainless steel parts: A statistical study. Journal of Materials Engineering and Performance. 23 (6), pp. 2139-2148. https://doi.org/10.1007/s11665-014-0993-9Glueing grids and clouds together: a service-oriented approach
Anjum, Ashiq, Hill, Richard, McClatchey, Richard, Bessis, Nik and Branson, Andrew 2012. Glueing grids and clouds together: a service-oriented approach. International Journal of Web and Grid Services. https://doi.org/10.1504/IJWGS.2012.049169Energy conservation in mobile devices and applications: a case for context parsing, processing and distribution in clouds
Kiani, Saad Liaquat, Anjum, Ashiq, Bessis, Nik, Hill, Richard and Knappmeyer, Michael 2013. Energy conservation in mobile devices and applications: a case for context parsing, processing and distribution in clouds. Mobile Information Systems.Approaching the Internet of things (IoT): a modelling, analysis and abstraction framework
Ikram, Ahsan, Anjum, Ashiq, Hill, Richard, Antonopoulos, Nikolaos, Liu, Lu and Sotiriadis, Stelios 2013. Approaching the Internet of things (IoT): a modelling, analysis and abstraction framework. Concurrency and Computation: Practice and Experience. https://doi.org/10.1002/cpe.3131Performance simulation of a context provisioning middleware based on empirical measurements
Reetz, Eike Steffen, Knappmeyer, Michael, Kiani, Saad Liaquat, Anjum, Ashiq, Bessis, Nik and Tönjes, Ralf 2012. Performance simulation of a context provisioning middleware based on empirical measurements. https://doi.org/10.1016/j.simpat.2012.03.002