Recent advancements in artificial intelligence (AI) and machine learning (ML) have significantly enhanced various fields, including intelligent transportation systems (ITS). A notable area of progress is vehicle class...
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Recent advancements in artificial intelligence (AI) and machine learning (ML) have significantly enhanced various fields, including intelligent transportation systems (ITS). A notable area of progress is vehicle classification, crucial for improving road safety and traffic management. Traditional vehicle classification methods often struggle with accuracy and efficiency in complex conditions. This study introduces a novel method using neuro-evolutionary algorithms (NEAs) to optimize vehicle classification, combining neural networks with evolutionary computation for robust framework design and parameter optimization. NEAs adaptively refine neural network architectures, particularly enhancing their performance in diverse driving scenarios. Implemented in Google Colab, our NEA-optimized models demonstrated a remarkable classification accuracy of 98.35%, outperforming traditional and contemporary methods. This approach not only advances vehicle classification accuracy but also sets the stage for future developments in ITS, promoting safer, more efficient mobility.
Short-term wind power prediction is challenging due to the chaotic characteristics of wind speed. Since, for wind power industries, designing an accurate and reliable wind power forecasting model is essential, we depl...
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Short-term wind power prediction is challenging due to the chaotic characteristics of wind speed. Since, for wind power industries, designing an accurate and reliable wind power forecasting model is essential, we deployed a novel composite deep learning-based evolutionary approach for accurate forecasting of the power output in wind-turbine farms, which is developed in three stages. At the beginning stage (pre -processing), the k-means clustering method and an autoencoder are employed to detect and filter noise in the SCADA measurements. In the Next step (decomposition), in order to decompose the SCADA time -series data, we proposed a new hybrid variational mode decomposition (HVMD) method, that consists of VMD and two heuristics: greedy Nelder-Mead search algorithm (GNM) and adaptive randomised local search (ARLS). Both heuristics are applied to tune the hyper-parameters of VMD that results in improving the performance of the forecasting model. In the third phase, based on prior knowledge that the un-derlying wind patterns are highly non-linear and diverse, we proposed a novel alternating optimisation algorithm that consists of self-adaptive differential evolution (SaDE) algorithm and sine cosine optimi-sation method as a hyper-parameter optimizer and then combine with a recurrent neural network (RNN) called Long Short-term memory (LSTM). This framework allows us to model the power curve of a wind turbine on a farm. A historical dataset from supervisory control and data acquisition (SCADA) systems were applied as input to estimate the power output from an onshore wind farm in Sweden. Two short time forecasting horizons, including 10 min ahead and 1 h ahead, are considered in our experiments. The achieved prediction results supported the superiority of the proposed hybrid model in terms of accurate forecasting and computational runtime compared with earlier published hybrid models applied in this paper. (c) 2021 Elsevier Ltd. All rights reserved.
The article L. Cornejo-Bueno, C. Camacho-Gomez, A. Aybar-Ruiz, L. Prieto, A. Barea-Ropero, S. Salcedo-Sanz, "Wind power ramp event detection with a hybrid neuro-evolutionary approach," Cornejo-Bueno, L., Cam...
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The article L. Cornejo-Bueno, C. Camacho-Gomez, A. Aybar-Ruiz, L. Prieto, A. Barea-Ropero, S. Salcedo-Sanz, "Wind power ramp event detection with a hybrid neuro-evolutionary approach," Cornejo-Bueno, L., Camacho-Gomez, C., Aybar-Ruiz, A. et al. Neural Comput & Applic (2018).
In today's modern world there are a number of different welding techniques that are getting used presently to join different similar as well as dissimilar metals. The number is growing further exponentially with g...
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In today's modern world there are a number of different welding techniques that are getting used presently to join different similar as well as dissimilar metals. The number is growing further exponentially with growing demands and rapid progress in science and technology. Friction stir welding (FSW) is relatively an advanced solid state joining process in which no consumable materials are used. It is energy efficient and environment friendly process in which the heat required is generated due to the friction between the rotating tool and workpiece material. In the present work, a low cost simple fixture was developed for locating and clamping workpiece in a milling machine to conduct FSW. It holds the workpiece securely, without any gap formation between the interface during tightening as well as during FSW operations. It does not allow the workpiece to move along the direction of tool travel, due to horizontal force. From the extensive experimental investigation and Taguchi's single response and different multi-objective analysis techniques, it was found that spindle speed (RPM), tool geometry (TG), and pin diameter (PnD) were the three most significant factors of the FSW process. The newly proposed fuzzy assisted grey relational analysis (FZ-GRA) method is found to be suitable for selecting the optimal process parameters in FSW process. Experimental investigation also showed that the tensile properties increase with increasing RPM for all the considered tool geometries especially at lower PnD. Threaded and straight cylindrical tools showed best and least mechanical properties, respectively. The nugget zone hardness value increased with RPM for a particular TG and PnD. The microstructure revealed the phenomena of dynamic recrystallization in NZ, deformed grains in thermo mechanically affected zone and expanded grains in heat affected zone. Fractrographic sample showed ductile fracture for base material as well as good welded joints whereas defective joints showed
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