All Outputs (636)

GANS-based data augmentation for citrus disease severity detection using deep learning (2020)
Journal Article
Zeng, Q., Ma, X., Cheng, B., Zhou, E., & Pang, W. (2020). GANS-based data augmentation for citrus disease severity detection using deep learning. IEEE Access, 8, 172882-172891. https://doi.org/10.1109/ACCESS.2020.3025196

Recently, many Deep Learning models have been employed to classify different kinds of plant diseases, but very little work has been done for disease severity detection. However, it is more important to master the severities of plant diseases accurate... Read More about GANS-based data augmentation for citrus disease severity detection using deep learning.

Predictive control using active aerodynamic surfaces to improve ride quality of a vehicle (2020)
Journal Article
Ahmad, E., Iqbal, J., Khan, M. A., Liang, W., & Youn, I. (2020). Predictive control using active aerodynamic surfaces to improve ride quality of a vehicle. Electronics, 9(9), Article 1463. https://doi.org/10.3390/electronics9091463

This work presents a predictive control strategy for a four degrees of freedom (DOF) half-car model in the presence of active aerodynamic surfaces. The proposed control strategy consists of two parts: the feedback control deals with the tracking erro... Read More about Predictive control using active aerodynamic surfaces to improve ride quality of a vehicle.

SafeML: Safety Monitoring of Machine Learning Classifiers Through Statistical Difference Measures (2020)
Journal Article
Aslansefat, K., Sorokos, I., Whiting, D., Tavakoli Kolagari, R., & Papadopoulos, Y. (2020). SafeML: Safety Monitoring of Machine Learning Classifiers Through Statistical Difference Measures. Lecture notes in computer science, 12297, 197-211. https://doi.org/10.1007/978-3-030-58920-2_13

Ensuring safety and explainability of machine learning (ML) is a topic of increasing relevance as data-driven applications venture into safety-critical application domains, traditionally committed to high safety standards that are not satisfied with... Read More about SafeML: Safety Monitoring of Machine Learning Classifiers Through Statistical Difference Measures.

An Integrated Approach to Support the Process-Based Certification of Variant-Intensive Systems (2020)
Journal Article
Bressan, L., de Oliveira, A. L., Campos, F., Papadopoulos, Y., & Parker, D. (2020). An Integrated Approach to Support the Process-Based Certification of Variant-Intensive Systems. Lecture notes in computer science, 12297 LNCS, 179-193. https://doi.org/10.1007/978-3-030-58920-2_12

© 2020, Springer Nature Switzerland AG. Component-based approaches and software product lines have been adopted by industry to manage the diversity of configurations on safety-critical software. Safety certification demands compliance with standards.... Read More about An Integrated Approach to Support the Process-Based Certification of Variant-Intensive Systems.

Failure Mode Reasoning in Model Based Safety Analysis (2020)
Journal Article
Jahanian, H., Parker, D., Zeller, M., McIver, A., & Papadopoulos, Y. (2020). Failure Mode Reasoning in Model Based Safety Analysis. Lecture notes in computer science, 12297 LNCS, 130-145. https://doi.org/10.1007/978-3-030-58920-2_9

© 2020, Springer Nature Switzerland AG. Failure Mode Reasoning (FMR) is a novel approach for analyzing failure in a Safety Instrumented System (SIS). The method uses an automatic analysis of an SIS program to calculate potential failures in parts of... Read More about Failure Mode Reasoning in Model Based Safety Analysis.

The Promise of Causal Reasoning in Reliable Decision Support for Wind Turbines (2020)
Presentation / Conference
Chatterjee, J., & Dethlefs, N. (2020, August). The Promise of Causal Reasoning in Reliable Decision Support for Wind Turbines. Paper presented at Fragile Earth: Data Science for a Sustainable Planet. KDD 2020, Virtual Conference

The global pursuit towards sustainable development is leading to increased adaptation of renewable energy sources. Wind turbines are promising sources of clean energy, but regularly suffer from failures and down-times, primarily due to the complex en... Read More about The Promise of Causal Reasoning in Reliable Decision Support for Wind Turbines.

Genetic Algorithms as a Feature Selection Tool in Heart Failure Disease (2020)
Journal Article
Alabed, A., Kambhampati, C., & Gordon, N. (in press). Genetic Algorithms as a Feature Selection Tool in Heart Failure Disease. Advances in Intelligent Systems and Computing, 1229 AISC, 531-543. https://doi.org/10.1007/978-3-030-52246-9_38

A great wealth of information is hidden in clinical datasets, which could be analyzed to support decision-making processes or to better diagnose patients. Feature selection is one of the data pre-processing that selects a set of input features by rem... Read More about Genetic Algorithms as a Feature Selection Tool in Heart Failure Disease.

High-quality vascular modeling and modification with implicit extrusion surfaces for blood flow computations (2020)
Journal Article
Hong, Q., Li, Q., Wang, B., Tian, J., Xu, F., Liu, K., & Cheng, X. (2020). High-quality vascular modeling and modification with implicit extrusion surfaces for blood flow computations. Computer Methods and Programs in Biomedicine, 196, Article 105598. https://doi.org/10.1016/j.cmpb.2020.105598

High-quality vascular modeling is crucial for blood flow simulations, i.e., computational fluid dynamics (CFD). As without an accurate geometric representation of the smooth vascular surface, it is impossible to make meaningful blood flow simulations... Read More about High-quality vascular modeling and modification with implicit extrusion surfaces for blood flow computations.

A Hybrid Modular Approach for Dynamic Fault Tree Analysis (2020)
Journal Article
Kabir, S., Aslansefat, K., Sorokos, I., Papadopoulos, Y., & Konur, S. (2020). A Hybrid Modular Approach for Dynamic Fault Tree Analysis. IEEE Access, 8, 97175-97188. https://doi.org/10.1109/ACCESS.2020.2996643

Over the years, several approaches have been developed for the quantitative analysis of dynamic fault trees (DFTs). These approaches have strong theoretical and mathematical foundations; however, they appear to suffer from the state-space explosion a... Read More about A Hybrid Modular Approach for Dynamic Fault Tree Analysis.

Operational Efficiency Improvement of PEM Fuel Cell - A Sliding Mode Based Modern Control Approach (2020)
Journal Article
Javaid, U., Mehmood, A., Arshad, A., Imtiaz, F., & Iqbal, J. (2020). Operational Efficiency Improvement of PEM Fuel Cell - A Sliding Mode Based Modern Control Approach. IEEE Access, 8, 95823-95831. https://doi.org/10.1109/ACCESS.2020.2995895

The efficiency and durability of a Proton Exchange Membrane Fuel Cell (PEMFC) can be improved with proper controller design to regulate the flow of reactants, cell stack temperature and humidity of the membrane. In this paper, sliding mode controller... Read More about Operational Efficiency Improvement of PEM Fuel Cell - A Sliding Mode Based Modern Control Approach.

Fingerprint enhancement using multi-scale classification dictionaries with reduced dimensionality (2020)
Journal Article
Bian, W., Xu, D., Cheng, Y., Li, Q., Luo, Y., & Yu, Q. (2020). Fingerprint enhancement using multi-scale classification dictionaries with reduced dimensionality. IET Biometrics, 9(5), 194-204. https://doi.org/10.1049/iet-bmt.2019.0121

In order to improve the quality of fingerprint with large noise, this paper proposes a fingerprint enhancement method by using a sparse representation of learned multi-scale classification dictionaries with reduced dimensionality. Mul... Read More about Fingerprint enhancement using multi-scale classification dictionaries with reduced dimensionality.

Locality Regularized Robust-PCRC: A Novel Simultaneous Feature Extraction and Classification Framework for Hyperspectral Images (2020)
Journal Article
Yang, Z., Cao, F., Cheng, Y., Ling, W., & Hu, R. (in press). Locality Regularized Robust-PCRC: A Novel Simultaneous Feature Extraction and Classification Framework for Hyperspectral Images. IEEE transactions on geoscience and remote sensing : a publication of the IEEE Geoscience and Remote Sensing Society, 1-16. https://doi.org/10.1109/tgrs.2020.2988900

Despite the successful applications of probabilistic collaborative representation classification (PCRC) in pattern classification, it still suffers from two challenges when being applied on hyperspectral images (HSIs) classification: 1) ineffective f... Read More about Locality Regularized Robust-PCRC: A Novel Simultaneous Feature Extraction and Classification Framework for Hyperspectral Images.

Local keypoint-based Faster R-CNN (2020)
Journal Article
Ding, X., Li, Q., Cheng, Y., Wang, J., Bian, W., & Jie, B. (in press). Local keypoint-based Faster R-CNN. Applied Intelligence, https://doi.org/10.1007/s10489-020-01665-9

Region-based Convolutional Neural Network (R-CNN) detectors have achieved state-of-the-art results on various challenging benchmarks. Although R-CNN has achieved high detection performance, the research of local information in producing candidates is... Read More about Local keypoint-based Faster R-CNN.

Deep learning with knowledge transfer for explainable anomaly prediction in wind turbines (2020)
Journal Article
Chatterjee, J., & Dethlefs, N. (2020). Deep learning with knowledge transfer for explainable anomaly prediction in wind turbines. Wind energy, 23(8), 1693-1710. https://doi.org/10.1002/we.2510

The last decade has witnessed an increased interest in applying machine learning techniques to predict faults and anomalies in the operation of wind turbines. These efforts have lately been dominated by deep learning techniques which, as in other fie... Read More about Deep learning with knowledge transfer for explainable anomaly prediction in wind turbines.

A constraint multi-objective evolutionary optimization of a state-of-the-art dew point cooler using digital twins (2020)
Journal Article
Golizadeh Akhlaghi, Y., Badiei, A., Zhao, X., Aslansefat, K., Xiao, X., Shittu, S., & Ma, X. (2020). A constraint multi-objective evolutionary optimization of a state-of-the-art dew point cooler using digital twins. Energy Conversion and Management, 211, Article 112772. https://doi.org/10.1016/j.enconman.2020.112772

This study is pioneered in developing digital twins using Feed-forward Neural Network (FFNN) and multi objective evolutionary optimization (MOEO) using Genetic Algorithm (GA) for a counter-flow Dew Point Cooler with a novel Guideless Irregular Heat a... Read More about A constraint multi-objective evolutionary optimization of a state-of-the-art dew point cooler using digital twins.

Hypnosis regulation in propofol anaesthesia employing super-twisting sliding mode control to compensate variability dynamics (2020)
Journal Article
Ilyas, M., Iqbal, J., Ahmad, S., Uppal, A. A., Imtiaz, W. A., & Riaz, R. A. (2020). Hypnosis regulation in propofol anaesthesia employing super-twisting sliding mode control to compensate variability dynamics. IET Systems Biology, 14(2), 59-67. https://doi.org/10.1049/iet-syb.2018.5080

Regulation of hypnosis level on bi-spectral index monitor (BIS) during a surgical procedure in propofol anaesthesia administration is a challenging task for an anaesthesiologist in multi-tasking environment of the operation theater. Automation in ana... Read More about Hypnosis regulation in propofol anaesthesia employing super-twisting sliding mode control to compensate variability dynamics.

Bio-AKA: An efficient fingerprint based two factor user authentication and key agreement scheme (2020)
Journal Article
Bian, W., Gope, P., Cheng, Y., & Li, Q. (2020). Bio-AKA: An efficient fingerprint based two factor user authentication and key agreement scheme. Future generations computer systems : FGCS, 109, 45-55. https://doi.org/10.1016/j.future.2020.03.034

The fingerprint has long been used as one of the most important biological features in the field of biometrics. It is person-specific and remain identical though out one’s lifetime. Physically uncloneable functions (PUFs) have been used in authentica... Read More about Bio-AKA: An efficient fingerprint based two factor user authentication and key agreement scheme.

Emerging challenges for HCI : enabling effective use of VR in education and training (2020)
Book Chapter
Gordon, N., & Brayshaw, M. (2020). Emerging challenges for HCI : enabling effective use of VR in education and training. In L. Bozgeyikli, & R. Bozgeyikli (Eds.), Virtual reality : recent advancements, applications and challenges. Gistrup, Denmark: River Publishers

This chapter considers some of the challenges in providing effective virtual reality (VR) environments for teaching and training, where users are encouraged and enabled to be truly engaged in their learning. One approach is to use inquiry-based learn... Read More about Emerging challenges for HCI : enabling effective use of VR in education and training.

Semantic Mapping for Model Transformation between AADL2 and HiP-HOPS (2020)
Conference Proceeding
Mian, Z., Gao, Y., Shi, X., & Tang, C. (2020). Semantic Mapping for Model Transformation between AADL2 and HiP-HOPS. In 4th International Conference on System Reliability and Safety (ICSRS) (539-543). https://doi.org/10.1109/ICSRS48664.2019.8987619

Currently, AADL has gradually become as one of the standards for the architecture design of complex embedded system. It is widely used in aerospace, automotive electronics and other fields for the design and analysis of high dependability-critical sy... Read More about Semantic Mapping for Model Transformation between AADL2 and HiP-HOPS.

A Cost Modeling Method Based on AADL2 (2020)
Conference Proceeding
Mian, Z., Tang, C., Gao, Y., Jia, S., Shi, X., & Chen, J. (2020). A Cost Modeling Method Based on AADL2. In 4th International Conference on System Reliability and Safety (ICSRS) (549-553). https://doi.org/10.1109/ICSRS48664.2019.8987612

The Architecture Analysis and Design Language (AADL) is widely used in the modeling, analysis and verification of the dependability-critical system. Previously, we have implemented the multi-objective (based on dependability and cost) architecture op... Read More about A Cost Modeling Method Based on AADL2.