This study presents an application of the self-organizing migrating algorithm (SOMA) to train artificial neural networks for skin segmentation tasks. We compare the performance of SOMA with popular gradient-based opti...
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This study presents an application of the self-organizing migrating algorithm (SOMA) to train artificial neural networks for skin segmentation tasks. We compare the performance of SOMA with popular gradient-based optimization methods such as ADAM and SGDM, as well as with another evolutionary algorithm, differential evolution (DE). Experiments are conducted on the skin dataset, which consists of 245,057 samples with skin and non-skin labels. The results show that the neural network trained by SOMA achieves the highest accuracy (93.18%), outperforming ADAM (84.87%), SGDM (84.79%), and DE (91.32%). The visual evaluation also reveals the SOMA-trained neural network's accurate and reliable segmentation capabilities in most cases. These findings highlight the potential of incorporating evolutionary optimization algorithms like SOMA into the training process of artificial neural networks, significantly improving performance in image segmentation tasks.
In a three-dimensional (3-D) model-based objects tracking and recognition system, the key problem of objects location is to establish the relationship between 2-D objects image and 3-D model. Based on 3-D model projec...
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ISBN:
(纸本)9781424427239
In a three-dimensional (3-D) model-based objects tracking and recognition system, the key problem of objects location is to establish the relationship between 2-D objects image and 3-D model. Based on 3-D model projection and 2-D image feature extraction, a modified Hausdorff distance is used to establish the matching function. The relationship between matching parameters are described with a probability model, and the distribution of parameter evolves towards the direction of dominant character through probability model learning and the corresponding operation, which is proposed to solve the problem of overmany iteration and slow constringency velocity The experiments show that the optimal matching parameters between 3-D model and 2-D image feature can be found accurately and efficiently, and then the accurate object location is completed.
In order to improve the precision of attitude operator in GPS attitude determination, based on Quantum-behaved Particle Swarm optimization(QPSO) algorithm, a new GPS carrier phase searching technology of attitude dete...
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ISBN:
(纸本)9781424421138
In order to improve the precision of attitude operator in GPS attitude determination, based on Quantum-behaved Particle Swarm optimization(QPSO) algorithm, a new GPS carrier phase searching technology of attitude determination is proposed. In favor of the ambiguity function method's fitness function, quantum behavior is introduced to enhance the ability of global searching to achieve the GPS fast determination. The simulations show the QPSO algorithm applied to solve benchmark functions is stable, fast of the searching speed and have a high accuracy. The actual application shows the method used in GPS attitude operator based on QPSO algorithm is able to search in the complex space, and the precision is high, the speed is rapid and the application effect is notable.
The bottleneck assignment (BA) and the generalized assignment (GA) problems and their exact solutions are explored in this paper. Firstly, a determinant elimination (DE) method is proposed based on the discussion of t...
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ISBN:
(纸本)9783037855454
The bottleneck assignment (BA) and the generalized assignment (GA) problems and their exact solutions are explored in this paper. Firstly, a determinant elimination (DE) method is proposed based on the discussion of the time and space complexity of the enumeration method for both BA and GA problems. The optimization algorithm to the pre-assignment problem is then discussed and the adjusting and transformation to the cost matrix is adopted to reduce the computational complexity of the DE method. Finally, a synthesis method for both BA and GA problems is presented. The numerical experiments are carried out and the results indicate that the proposed method is feasible and of high efficiency.
This paper is dedicated to the fundamental research of the mechanical model of a 1/4-vehicle semi-active suspension system with time-delayed state feedback control during wheel vertical displacement. The strategy comb...
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This paper is dedicated to the fundamental research of the mechanical model of a 1/4-vehicle semi-active suspension system with time-delayed state feedback control during wheel vertical displacement. The strategy combining the "equivalent harmonic excitation" optimization algorithm with the particle swarm optimization algorithm is proposed in this paper. Through the optimization and solution of time-delayed feedback control parameters of the 1/4 vehicle semi-active suspension system, the dynamic response of the vehicle suspension system before and after parameter optimization is studied. The research results indicate that, compared to passive control, time-delayed feedback control of wheel vertical displacement can significantly improve the smoothness, handling stability, and safety of vehicle operation.
With the rapid development of internet technology, the amount of collected or generated data has increased exponentially. The sheer volume, complexity, and unbalanced nature of this data pose a challenge to the scient...
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With the rapid development of internet technology, the amount of collected or generated data has increased exponentially. The sheer volume, complexity, and unbalanced nature of this data pose a challenge to the scientific community to extract meaningful information from this data within a reasonable time. In this paper, we implemented a scalable design of an artificial bee colony for big data classification using Apache Spark. In addition, a new fitness function is proposed to handle unbalanced data. Two experiments were performed using the real unbalanced datasets to assess the performance and scalability of our proposed algorithm. The performance results reveal that our proposed fitness function can efficiently deal with unbalanced datasets and statistically outperforms the existing fitness function in terms of G-mean and F-1-Score. In additon, the scalability results demonstrate that our proposed Spark-based design obtained outstanding speedup and scaleup results that are very close to optimal. In addition, our Spark-based design scales efficiently with increasing data size.
Accurate forecasting of natural gas consumption (NGC) plays an important role in energy supply, energy trading, economic effects and environmental sustainability. NGC forecasts can be used to adjust production and sup...
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Accurate forecasting of natural gas consumption (NGC) plays an important role in energy supply, energy trading, economic effects and environmental sustainability. NGC forecasts can be used to adjust production and supply plans to improve gas efficiency and reduce carbon emissions and supply chain waste. This paper reviews the research progress on NGC in the past decade, analyzes the typical characteristics of different forecasting strategies, and highlights 163 studies in terms of the technical aspects of feature processing methods, data decomposition methods, forecasting models and optimization algorithms. It also systematically elaborates the application of statistical models, machine learning models, grey models, logistic regression and their combinations in predictive models. Bibliometric methods are also utilized to dissect research hotspots and summarize cutting-edge trends in the field. It is worth mentioning that in the terms of hybrid model structures, the application and performance of various model structures are described and evaluated. In this paper, the future development is discussed from spatiotemporal characteristics, studying reasonable data decomposition layers and fusion models, considering potential data privacy issues, and developing artificial intelligence-supporting models and interpretable frameworks. This paper is expected to provide a multi-technology reference for natural gas forecasting and help researchers to select and develop more accurate forecasting techniques and strategies.
In this research work, Friction Theory and Free Volume Theory are applied to live oil characterized based on SARA TEST for viscosity modeling and make a new model in combination with two equation of state (PR and PCSA...
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In this research work, Friction Theory and Free Volume Theory are applied to live oil characterized based on SARA TEST for viscosity modeling and make a new model in combination with two equation of state (PR and PCSAFT). Parameters for pseudo-components are obtained by tuning the viscosity at atmospheric pressure and temperatures of 10, 20, and 40 ?. A new fitting approach is suggested where the number of fitting parameters is 17 and 12 for FT and FVT model, respectively. These parameters are tuned using the Genetic algorithm and Particle Swarm optimization and make eight new models. The results show that PC-SAFT provides viscosity predictions for all models with less deviation from experimental viscosity. The FT and FVT models have less error for oils with API > 40 and API < 40, respectively. The PC-SAFT + PSO improves the accuracy in viscosity modeling for both FT and FVT models. PSO can play a significant role even more than PC-SAFT.
This paper presents a simple, efficient, real number encoding genetic algorithm. The algorithm has omitted the workload of encoding and selection, it adopts deterministic induced crossover and mutation operators to im...
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ISBN:
(纸本)9780769547923
This paper presents a simple, efficient, real number encoding genetic algorithm. The algorithm has omitted the workload of encoding and selection, it adopts deterministic induced crossover and mutation operators to improve the algorithm's ability of local convergence;And introduced foreign populations by the theory of the bee evolution genetic algorithm, which has strengthened the capacity of mining the information contained in the population optimal individual. This algorithm is not need to improve the overall fitness of the population, but using genetic algorithm processes to achieve the optimal search. We have verified the algorithm through JAVA and MATLAB, the results show that this algorithm can obtain the optimal solution within certain accuracy in 10 generations.
In a fuzzy cognitive map-based forecasting model, causal relationships (represented with a weight matrix) are constant. This may hinder the applicability of such a model. In this paper, we propose an adaptive fuzzy co...
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ISBN:
(数字)9781728123486
ISBN:
(纸本)9781728123493
In a fuzzy cognitive map-based forecasting model, causal relationships (represented with a weight matrix) are constant. This may hinder the applicability of such a model. In this paper, we propose an adaptive fuzzy cognitive map-based forecasting model. Different from the existing models, the proposed model is made of a collection of fuzzy cognitive maps. Maps are constructed according to the clustering results of the so-called premises covering an entire time series. Subsequently, we use an optimization algorithm to train parameters of each fuzzy cognitive map individually. The proposed model construction procedure allows forming fuzzy cognitive maps that more flexible and, thus, suitable for forecasting of long time series. In experimental studies on synthetic time series and real time series, the proposed model performed very well in comparison with the original fuzzy cognitive map-based forecasting model and another two forecasting models.
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