An accurate assessment of the state of health (SOH) is the cornerstone for guaranteeing the long-term stable operation of electrical equipment. However, the noise the data carries during cyclic aging poses a severe ch...
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An accurate assessment of the state of health (SOH) is the cornerstone for guaranteeing the long-term stable operation of electrical equipment. However, the noise the data carries during cyclic aging poses a severe challenge to the accuracy of SOH estimation and the generalization ability of the model. To this end, this paper proposed a novel SOH estimation model for lithium-ion batteries that incorporates advanced signal-processing techniques and optimizedmachine-learning strategies. The model employs a whale optimization algorithm (WOA) to seek the optimal parameter combination (K, alpha) for the variational modal decomposition (VMD) method to ensure that the signals are accurately decomposed into different modes representing the SOH of batteries. Then, the excellent local feature extraction capability of the convolutional neural network (CNN) was utilized to obtain the critical features of each modal of SOH. Finally, the support vector machine (SVM) was selected as the final SOH estimation regressor based on its generalization ability and efficient performance on small sample datasets. The method proposed was validated on a two-class publicly available aging dataset of lithium-ion batteries containing different temperatures, discharge rates, and discharge depths. The results show that the WOA-VMD-based data processing technique effectively solves the interference problem of cyclic aging data noise on SOH estimation. The CNN-SVM optimized machine learning method significantly improves the accuracy of SOH estimation. Compared with traditional techniques, the fused algorithm achieves significant results in solving the interference of data noise, improving the accuracy of SOH estimation, and enhancing the generalization ability.
The present study introduces optimized machine learning (OML) models for predicting the ultimate axial load-carrying capacity of square concrete-filled steel tube (SCFST) columns. The structural performance of concret...
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machinelearning is widely used for securing cyber physical systems, but their computational demands pose challenges in constrained environments. This paper reviews and analyzes optimization methods for learning model...
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machinelearning is widely used for securing cyber physical systems, but their computational demands pose challenges in constrained environments. This paper reviews and analyzes optimization methods for learning models, including feature engineering, hyper-parameter optimization, and optimizer selection. It also proposes a methodology to utilize optimizedlearning models for cyber physical system security. By optimizing learning models, they become feasible for deployment in constrained systems. The paper explores various optimization techniques to enhance learning model efficiency and effectiveness. Feature engineering transforms raw data into meaningful representations, hyper-parameter optimization finds optimal configuration settings, optimizer selection chooses the most suitable algorithm, and federated learning reduces the node level computational requirements and also preserves privacy of individual nodes. The proposed methodology integrates optimized models into cyber physical systems to improve security, considering their resource limitations. This research contributes to the advancement of machinelearning in securing cyber physical systems. The findings highlight the benefits of optimization methods and emphasize the importance of optimizedlearning models in constrained environments, enhancing security measures.
Monitoring of real estate growth is essential with the increasing demand for housing and working space in cities. In this study, a new methodological framework is proposed to map the area under real estate using geosp...
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Monitoring of real estate growth is essential with the increasing demand for housing and working space in cities. In this study, a new methodological framework is proposed to map the area under real estate using geospatial techniques. In this framework, the built-up area and open land at successive stages of development are used to map the area under real estate. Three machinelearning algorithms were used, namely random forest (RF), support vector machine (SVM), and feedforward neural networks (FFNN), to classify the land use and land cover (LULC) map of Delhi NCR during 1990-2018, which is the basic input for real estate mapping. The results of the study show that optimized RF performed better than SVM and FFNN in LULC classification. The real estate land increased by 279% in Delhi NCR during 1990-2018. The area under real estate increased by 33%, 47%, 29%, 21%, and 22% during 1990-1996, 1996-2003, 2003-2008, 2008-2014, and 2014-2018, respectively. Among the cities surrounding Delhi, Gurgaon, Rohtak, Noida, and Faridabad have witnessed maximum real estate growth. The approach used in this study could be used for real estate mapping in other cities across the world.
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