This paper proposes a new hybridised approach comprising the flower pollination algorithm and pathfinder algorithm (FPAPFA), in order to address optimisation problems and for load frequency control system. Although th...
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This paper proposes a new hybridised approach comprising the flower pollination algorithm and pathfinder algorithm (FPAPFA), in order to address optimisation problems and for load frequency control system. Although the FPA is a popular algorithm that has been widely used in diverse applications, its implementation is met with the tendency to be trapped in local optimal due to an imbalance between the exploration and exploitation process. Consequently, the FPA's exploration functionality can be enhanced by using the PFA features to shift certain pollens to moderately enhanced locations rather than leading them to random positions. Furthermore, a modified fluctuation rate is incorporated into the PFA to reinforce the exploitative competence of the FPAPFA. Compared to other popular techniques, the proposed algorithm's performance was evaluated against 23 standard mathematical optimisation functions and statistically tested using the Wilcoxon rank-sum and Friedman rank tests. Moreover, the FPAPFA is applied to regulate two unequal multi-area interconnected power systems with different generating units (thermal, hydro, diesel, and wind power plants) while also integrating redox flow batteries (RFBs) and interline power flow controller (IPFC). Simulation results show that the proposed FPAPFA delivered better results than other algorithms with improved convergence speed, stability, and robustness.
Speech impairment limits a person's capacity for oral and auditory communication. Improvements in communication between the deaf and the general public can be progressed by a real-time sign language detector. Rece...
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Speech impairment limits a person's capacity for oral and auditory communication. Improvements in communication between the deaf and the general public can be progressed by a real-time sign language detector. Recent studies have contributed to make progress in motion and gesture identification processes using Deep Learning (DL) methods and computer vision. But the development of static and dynamic sign language recognition (SLR) models is still a challenging area of research. The difficulty is in obtaining an appropriate model that addresses the challenges of continuous signs that are independent of the signer. Different signers' speeds, durations, and many other factors make it challenging to create a model with high accuracy and continuity. This study mainly focused on SLR using a modified DL and hybrid optimization approach. Notably, spatial and geometric-based features are extracted via the Visual Geometry Group 16 (VGG16), and motion features are extracted using the optical flow approach. A new DL model, CNNSa-LSTM, is a combination of a Convolutional Neural Network (CNN), Self-Attention (SA), and Long-Short-Term Memory (LSTM) to identify sign language. This model is developed for feature extraction by combining CNNs for spatial analysis with SA mechanisms for focusing on relevant features, while LSTM effectively models temporal dependencies. The proposed CNNSa-LSTM model enhances performance in tasks involving complex, sequential data, such as sign language processing. Besides, a Hybrid Optimizer (HO) is proposed using the Hippopotamus Optimization algorithm (HOA) and the pathfinder algorithm (PFA). The proposed model has been implemented in Python, and it has been evaluated over the existing models in terms of accuracy (98.7%), sensitivity (98.2%), precision (98.5%), Word Error Rate (WER) (0.131), Sign Error Rate (SER) (0.114), and Normalized Discounted Cumulative Gain (NDCG) (98%) as well. The proposed model has recorded the highest accuracy of 98.7%.
Thermodynamic problems are usually formulated as optimization problems but are often nonlinear and non -convex and require robust optimization techniques to find the global solution. This work studied four newly devel...
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Thermodynamic problems are usually formulated as optimization problems but are often nonlinear and non -convex and require robust optimization techniques to find the global solution. This work studied four newly developed swarm-based stochastic optimization algorithms, which are Honey badger algorithms (HBA), Path-finder algorithms (PFA), Horse herd optimization algorithms (HOA) and Red fox optimization (RFO). They were applied to solving several phase stability and equilibrium problems with different degrees of complexity. The strengths and weaknesses of all the algorithms were compared. The stochastic algorithms solved the problems and obtained the global minimum value with a high success rate. HBA and HOA performed very well in all the problems considered, though the performance of HBA was slightly better than that of HOA. The performance of PFA closely followed while RFO was poor and had the worst performance among the four algorithms, but with the inclusion of a local optimizer, there was a significant improvement. Therefore, HBA and HOA are robust and reliable for solving phase stability and equilibrium problems.
Capturing the thermal emission from the Earth, satellite sensors permit to derive sea surface temperature (SST). The infrared radiance intercepted by the sensor also depends on other variables, such as the surface emi...
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ISBN:
(纸本)9798350379013;9798350379006
Capturing the thermal emission from the Earth, satellite sensors permit to derive sea surface temperature (SST). The infrared radiance intercepted by the sensor also depends on other variables, such as the surface emissivity, viewing geometry and the atmospheric contribution. All variables that affect atmospheric absorption and emission must be carefully considered. The brightness temperature of the surface measured by satellite sensor needs transformation to obtain SST. To remove the noise generated by the atmosphere, an equation can be used to derive a set of SST algorithm coefficients that can be applied to the brightness temperature image. Temperature data from specific points are necessary for the scope. This article aims to demonstrate that, in absence of an adequate temperature dataset, SST can be derived by MODIS thermal images (pixel dimensions: 1 km x 1 km) using Copernicus data even if it has a lower spatial resolution (0.0625 degrees x 0.0625 degrees). The study area includes an extended part of the Mediterranean Sea which contains the Strait of Sicily, the Adriatic, Ionian, and Tyrrhenian Seas. The pathfinder algorithm developed by NOAA based on an equation with pre-established coefficients is also applied for comparison. Temperatures recorded by buoys are used to test the result accuracy. The experiments testify the good performance of the adopted approach.
Perch is a relatively valuable aquatic product with high economic value. Dissolved oxygen follows a complex, dynamic and non-linear system. To solve the problems of low prediction accuracy and poor generalization abil...
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Perch is a relatively valuable aquatic product with high economic value. Dissolved oxygen follows a complex, dynamic and non-linear system. To solve the problems of low prediction accuracy and poor generalization ability of traditional dissolved oxygen prediction methods, a dissolved oxygen hybrid prediction model for perch culture water quality based on principal component analysis and pathfinder optimization algorithm is proposed in this paper. Firstly, the key influencing factors affecting the dissolved oxygen of bass were extracted by PCA to eliminate redundant variables and reduce the data dimension and complexity. Then the PFA optimization algorithm is used to automatically optimize the key parameters of GRU neural network to obtain the optimal parameter combination. Finally, a combined prediction model based on PCA-PFA-GRU is constructed to predict the dissolved oxygen in perch culture water quality. The MSE, MAE, RMSE and R-2 are 0.010, 0.060, 0.100 and 0.983, respectively. The simulation results show that the proposed PCA-PFA-GRU model has a small fluctuation of prediction error and high prediction accuracy. In conclusion, the proposed model has good prediction accuracy and generalization and has achieved excellent prediction effect in short-term prediction to avoid huge losses, reduce growth risks and promote the development of fishery modernization.
To solve the problem of the fuzzy and dynamics of requirement caused by users' cognitive bias, a dynamic requirement and priority capture method based on user scenarios is proposed, aiming at effectively improving...
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To solve the problem of the fuzzy and dynamics of requirement caused by users' cognitive bias, a dynamic requirement and priority capture method based on user scenarios is proposed, aiming at effectively improving user experience. The method consists of the following steps: Firstly, users with similar characteristics are filtered to form a user cluster, then obtain the user's product experience in different usage scenarios and acquire preliminary requirements by using service design methods. Secondly, the requirement path model tree will be designed and the requirement path matrix will be constructed through the evaluation of the user cluster. Then the pathfinder algorithm will be used to calculate the required correlation of user clusters and prioritize the requirements. Finally, the direction of the product design will be provided. Taking the design of the intelligent office chair as an example, the effectiveness of the method is verified by evaluating the satisfaction of user experience.
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