Crucial for carbon capture, utilization, and storage (CCUS) initiatives and diverse industries, heat transfer underscores the need for a precise assessment of carbon dioxide (CO2) and nitrogen (N2) viscosities in gase...
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Crucial for carbon capture, utilization, and storage (CCUS) initiatives and diverse industries, heat transfer underscores the need for a precise assessment of carbon dioxide (CO2) and nitrogen (N2) viscosities in gaseous blends across various temperatures. This research pioneers an intelligent model by enhancing the dendritic neural regression (DNR) framework, employing the seagull optimization algorithm with Marine Predator algorithm (SOAMPA) for optimal predictions. Leveraging recent advancements in metaheuristic optimization techniques, the study reveals the superior performance of the novel SOAMPA approach in predictive accuracy, marking a significant breakthrough in predicting CO2-N2 mixture viscosities with implications for advancing CCUS projects and diverse industries. The optimized DNR model, empowered by the modified SOAMPA optimization technique, contributes to estimating the viscosity of N2-CO2 mixture gases. Utilizing inputs like pressure, temperature, mole fraction of N2, and model fraction of CO2, the models are trained and tested on a dataset comprising over 3030 data samples from public literature. Key contributions encompass proposing an optimized DNR approach, introducing the modified SOAMPA technique, and demonstrating its superiority over established optimization methods in conjunction with the traditional DNR model for predicting viscosity based on real experimental datasets.
Aiming at the problem that Ant Colony optimization (ACO) is subject primarily to the parameters, we propose a hybrid algorithm SOA-ACO-2Opt to optimize the ACO parameter combination through seagulloptimization Algori...
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Aiming at the problem that Ant Colony optimization (ACO) is subject primarily to the parameters, we propose a hybrid algorithm SOA-ACO-2Opt to optimize the ACO parameter combination through seagull optimization algorithm (SOA) to strengthen ACO's search capability. To obtain a uniform initial distribution of the ACO parameter combination, we incorporated the Kent Chaos Map (KCM) to randomly initialize the seagull's position, reducing the tendency of SOA to fall into the local optimum. To avoid slow calculation speed and premature convergence of ACO, we improved the adaptive multi-population mechanism to reduce repeated redundant calculations and used the epsilon - greed y and default strategy, respectively, to update the ants' position. 2Opt is applied to find shorter paths in each iteration. In addition, when AUV navigates on the planned path, it may encounter obstacles. Therefore, this paper proposes an autonomous obstacle avoidance algorithm based on forward-looking sonar to ensure safety during tasks. SOA-ACO-2Opt is verified against twelve different problems extracted from TSPLIB and compared with some state-of-the-art algorithms. Furthermore, sea trials were carried out for several representative marine engineering applications of TSP and obstacle avoidance. Experimental results show that this work can significantly improve AUV's work efficiency and intelligence and protect the AUV's safety.
The extensive adoption of cloud computing (CC) enabled healthcare systems in attaining medical data from different data sources sustained by heterogeneous cloud providers. Data deduplication (DD) is a proficient metho...
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The extensive adoption of cloud computing (CC) enabled healthcare systems in attaining medical data from different data sources sustained by heterogeneous cloud providers. Data deduplication (DD) is a proficient method to effectually share and store data in the cloud server. At the same time, deep learning (DL) models can be applied for effective decision-making in the cloud-based healthcare system. With this motivation, this study develops an Intelligent DD with Deep Transfer Learning Enabled Classification Model for Cloud-based Healthcare System (IDDTLC-CHS) model. The presented IDDTLC-CHS model aims to accomplish effective DD and classifi-cation in the cloud-enabled healthcare environment. For DD, the neighbourhood correlation sequence (NCS) algorithm is employed which generates optimum code words and is then compressed by Deflate model. Besides, the data classification module involves a series of processes namely fuzzy c-means (FCM) segmentation, Xception feature extraction;bidirectional gated recurrent unit (BiGRU), and seagull optimization algorithm (SGO). The SGO algorithm is applied for optimal adjustment of the parameters involved in the BiGRU model. To assess enhanced outcomes of the IDDTLC-CHS model, a wide-ranging simulation analysis is carried out using the benchmark dataset. The comparative analysis reported the betterment of the IDDTLC-CHS model compared to other recent approaches.
To address challenges encountered in traditional Elman neural networks (ENNs), such as low convergence accuracy, difficulties in hyperparameter selection, and issues with gradient disappearance, a new ENN based on the...
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To address challenges encountered in traditional Elman neural networks (ENNs), such as low convergence accuracy, difficulties in hyperparameter selection, and issues with gradient disappearance, a new ENN based on the Gaussian kernel and an improved seagull optimization algorithm (SOA) named GSENN is proposed. First, the principle of the ENN is introduced, and the effects of the network structure, parameters, and gradient descent algorithm on its output are analyzed. Building upon the analysis results, an input layer for the ENN using the Gaussian kernel is designed. The outstanding local feature extraction capability of the Gaussian kernel is used to enhance convergence accuracy, and the predicted trends are more in line with real data. In addition, we propose an SOA incorporating a nonlinear factor to optimize the weights and thresholds of the ENN. This approach aims to resolve the challenges in hyperparameter selection and problems of gradient vanishing in the ENN. Seven commonly used neural network structures and the latest improved techniques were chosen for comparative experiments across different datasets. The experimental results show that GSENN achieves a mean relative error rate of 4.286 %. The MSE results indicate a 45.600 % improvement over the original ENN and a 37.142 % improvement over BP neural networks. The experimental results demonstrate that GSENN exhibits efficient forecasting capability and offers reliable and effective forecasting for the relevant departments.
The objective of smart power systems is to combine all renewable energy sources in order to increase the electricity supply of clean energy sources. This paper proposes an optimization model for minimizing the energy ...
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The objective of smart power systems is to combine all renewable energy sources in order to increase the electricity supply of clean energy sources. This paper proposes an optimization model for minimizing the energy cost (EC) and enhancing the power supply for rural areas by designing and analyzing three different hybrid system configurations based on integrating a biomass system with a photovoltaic (PV), wind turbine (WT) and battery system. The first hybrid system includes PV, WT, Biomass generator, and Battery storage device;the second configuration includes PV with Biomass and Battery, and the last one includes WT with Biomass and Battery. The control parameters are kept the same for both algorithms in all case studies. Real-time meteorological data are used for a remote area located in the western desert of Egypt called Abu-Monqar village. Four recent optimizationalgorithms, namely Slime Mould algorithm (SMA), seagull optimization algorithm (SOA), gray Wolf Optimizer (GWO), Whale optimizationalgorithm (WOA), and Sine Cosine algorithm (SCA) are utilized and compared with each other to ensure that all load demand is met at the lowest energy cost (EC) for the proposed hybrid system. Based on the comparison of the obtained results and the convergence curves for the three scenarios revealed that the SMA outperformed the other algorithms in terms of the best objective function. The obtained results revealed that the third scenario using SMA method provides the optimal configuration in terms of the net present cost (NPC), EC, and LPSP with 3,476,371.76$, 0.1186861 $ /kWh, and 0.032493, respectively. 1) Motivations
In this paper, a seagull optimization algorithm (SOA) based 3-Degree-of-freedom (DOF) proportional-integral-derivative (3DOFPID) controller is suggested for load frequency control of multi-area interconnected power sy...
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In this paper, a seagull optimization algorithm (SOA) based 3-Degree-of-freedom (DOF) proportional-integral-derivative (3DOFPID) controller is suggested for load frequency control of multi-area interconnected power system (MAIPS). The considered MAIPS comprises of two areas with Thermal-Hydro-Nuclear generation units in each area. Analysis has been carried out by subjugating area-1 of MAIPS with a step load disturbance (SLD) of 10%. The sovereignty of presented SOA tuned 3DOFPID in regulating the stability of MAIPS is revealed upon comparing with the performances of 2DOFPID and conventional PID controllers. MIPS is analyzed dynamically without and with considering the nonlinear realistic constraint of communication time delays (CTDs) to demonstrate its impact on load frequency control performance. Simulation results disclosed that, MAIPS dynamical behavior is slightly more deviated up on considering CTDs and is justified.
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