TY  - JOUR
A1  - Khayyam, Hamid
A1  - Jamali, Ali
A1  - Bab-Hadiashar, Alireza
A1  - Esch, Thomas
A1  - Ramakrishna, Seeram
A1  - Jalili, Mahdi
A1  - Naebe, Minoo
T1  - A Novel Hybrid Machine Learning Algorithm for Limited and Big Data Modeling with Application in Industry 4.0
JF  - IEEE Access
N2  - To meet the challenges of manufacturing smart products, the manufacturing plants have been radically changed to become smart factories underpinned by industry 4.0 technologies. The transformation is assisted by employment of machine learning techniques that can deal with modeling both big or limited data. This manuscript reviews these concepts and present a case study that demonstrates the use of a novel intelligent hybrid algorithms for Industry 4.0 applications with limited data. In particular, an intelligent algorithm is proposed for robust data modeling of nonlinear systems based on input-output data. In our approach, a novel hybrid data-driven combining the Group-Method of Data-Handling and Singular-Value Decomposition is adapted to find an offline deterministic model combined with Pareto multi-objective optimization to overcome the overfitting issue. An Unscented-Kalman-Filter is also incorporated to update the coefficient of the deterministic model and increase its robustness against data uncertainties. The effectiveness of the proposed method is examined on a set of real industrial measurements.
Y1  - 2020
U6  - http://dx.doi.org/10.1109/ACCESS.2020.2999898
SN  - 2169-3536
VL  - 8
IS  - Art. 9108222
SP  - 111381
EP  - 111393
PB  - IEEE
CY  - New York, NY
ER  - 
TY  - JOUR
A1  - Khayyam, Hamid
A1  - Jamali, Ali
A1  - Bab-Hadiashar, Alireza
A1  - Esch, Thomas
A1  - Ramakrishna, Seeram
A1  - Jalil, Mahdi
A1  - Naebe, Minoo
T1  - A Novel Hybrid Machine Learning Algorithm for Limited and Big Data Modelling with Application in Industry 4.0
JF  - IEEE Access
N2  - To meet the challenges of manufacturing smart products, the manufacturing plants have been radically changed to become smart factories underpinned by industry 4.0 technologies. The transformation is assisted by employment of machine learning techniques that can deal with modeling both big or limited data. This manuscript reviews these concepts and present a case study that demonstrates the use of a novel intelligent hybrid algorithms for Industry 4.0 applications with limited data. In particular, an intelligent algorithm is proposed for robust data modeling of nonlinear systems based on input-output data. In our approach, a novel hybrid data-driven combining the Group-Method of Data-Handling and Singular-Value Decomposition is adapted to find an offline deterministic model combined with Pareto multi-objective optimization to overcome the overfitting issue. An Unscented-Kalman-Filter is also incorporated to update the coefficient of the deterministic model and increase its robustness against data uncertainties. The effectiveness of the proposed method is examined on a set of real industrial measurements.
Y1  - 2020
SN  - 2169-3536
U6  - http://dx.doi.org/10.1109/ACCESS.2020.2999898
SP  - 1
EP  - 12
PB  - IEEE
CY  - New York, NY
ER  - 
TY  - JOUR
A1  - Fayyazi, Mojgan
A1  - Sardar, Paramjotsingh
A1  - Thomas, Sumit Infent
A1  - Daghigh, Roonak
A1  - Jamali, Ali
A1  - Esch, Thomas
A1  - Kemper, Hans
A1  - Langari, Reza
A1  - Khayyam, Hamid
T1  - Artificial intelligence/machine learning in energy management systems, control, and optimization of hydrogen fuel cell vehicles
N2  - Environmental emissions, global warming, and energy-related concerns have accelerated the advancements in conventional vehicles that primarily use internal combustion engines. Among the existing technologies, hydrogen fuel cell electric vehicles and fuel cell hybrid electric vehicles may have minimal contributions to greenhouse gas emissions and thus are the prime choices for environmental concerns. However, energy management in fuel cell electric vehicles and fuel cell hybrid electric vehicles is a major challenge. Appropriate control strategies should be used for effective energy management in these vehicles. On the other hand, there has been significant progress in artificial intelligence, machine learning, and designing data-driven intelligent controllers. These techniques have found much attention within the community, and state-of-the-art energy management technologies have been developed based on them. This manuscript reviews the application of machine learning and intelligent controllers for prediction, control, energy management, and vehicle to everything (V2X) in hydrogen fuel cell vehicles. The effectiveness of data-driven control and optimization systems are investigated to evolve, classify, and compare, and future trends and directions for sustainability are discussed.
KW  - optimization system
KW  - intelligent control
KW  - fuel cell vehicle
KW  - machine learning
KW  - artificial intelligence
KW  - intelligent energy management
Y1  - 2023
U6  - http://dx.doi.org/10.3390/su15065249
N1  - This article belongs to the Special Issue "Circular Economy and Artificial Intelligence"
VL  - 15
IS  - 6
SP  - 38
PB  - MDPI
CY  - Basel
ER  -