In this paper, inspired by the analysis of convergence PSO, we study individual particle a PSO system moving in quantum multidimensional space and establish delta potential well model for PSO. After that, trial method parameter control QDPSO is proposed. The experiment result shows much advantage to traditional

Based on the quantum-behaved particle swarm optimization (QPSO) algorithm, we formulate philosophy of QPSO and introduce a so-called mainstream thought population to evaluate search scope thus propose novel parameter control method QPSO. After that, test revised algorithm several benchmark functions experiment results show its superiority.

Quantum-behaved particle swarm optimization (QPSO), motivated by concepts from quantum mechanics and (PSO), is a probabilistic algorithm belonging to the bare-bones PSO family. Although it has been shown perform well in finding optimal solutions for many problems, there so far little analysis on how works detail. This paper presents comprehensive of QPSO algorithm. In theoretical analysis, we analyze behavior single terms probability measure. Since particle's influenced contraction-expansion...

In order to effectively identify and classify weld defects of thin-walled metal canisters, a defect detection classification algorithm based on machine vision is proposed in this paper. With the categorized, modified background subtraction method Gaussian mixture models, extract feature areas defects. Then, we design an for according extracted features. Next, by using images sampled constructed system real-world production line, parameters classifiers are determined empirically. Experimental...

Brain tumors are a pernicious cancer with one of the lowest five-year survival rates. Neurologists often use magnetic resonance imaging (MRI) to diagnose type brain tumor. Automated computer-assisted tools can help them speed up diagnosis process and reduce burden on health care systems. Recent advances in deep learning for medical have shown remarkable results, especially automatic instant various cancers. However, we need large amount data (images) train models order obtain good results....

This paper proposes the random drift particle swarm optimization (RDPSO) algorithm to solve economic dispatch (ED) problems from power systems area. The RDPSO is inspired by free electron model in metal conductors placed an external electric field, and it employs a novel set of evolution equations that can enhance global search ability algorithm. Many nonlinear characteristics generator, such as ramp rate limits, prohibited operating zones nonsmooth cost functions are considered when...

Hyperspectral image super-resolution addresses the problem of fusing a low-resolution hyperspectral (LR-HSI) and high-resolution multispectral (HR-MSI) to produce (HR-HSI). In this paper, we propose novel fusion approach for by exploiting specific properties matrix decomposition, which consists four main steps. First, an endmember extraction algorithm is used extract initial spectral from LR-HSI. Then, with matrix, estimate spatial i.e., spatial-contextual information, degraded observations...

This paper focuses on the feature gene selection for cancer classification, which employs an optimization algorithm to select a subset of genes. We propose binary quantum-behaved particle swarm (BQPSO) selection, coupling support vector machine (SVM) classification. First, proposed BQPSO is described, discretized version original QPSO 0-1 problems. Then, we present principle and procedure classification based SVM with leave-one-out cross validation (LOOCV). Finally, (BQPSO/SVM), PSO...

The vast majority of accidents in construction are generated by unsafe behaviors. Some researches also find that the behaviors could be influenced awareness and safety climate. behavior belong to individual levels, while climate belongs organization level. Previous studies mainly focus on relationships between climate, awareness, without considering their different respective levels interaction levels. This study establishes a hierarchical linear model (HLM) examine multilevel them. Data...

Hyperspectral and multispectral image fusion aims to fuse a low-spatial-resolution hyperspectral (HSI) high-spatial-resolution form HSI. Motivated by the success of model- deep learning-based approaches, we propose novel patch-aware approach for HSI unfolding subspace-based optimization model, where moderate-sized patches are used in both training test phases. The goal this is make full use information patch under subspace representation, restrict scale enhance interpretability network,...

Heuristic algorithms have been developed to find approximate solutions for high-utility itemset mining (HUIM) problems that compensate the performance bottlenecks of exact algorithms. However, heuristic still face problem long runtime and insufficient quality, especially large transaction datasets with thousands tens items up millions transactions. To solve these problems, a novel GPU-based efficient parallel algorithm HUIM (PHA-HUIM) is proposed in this paper. The iterative process PHA-HUIM...

In this work, we propose a deep learning-based model for mapping between the data of flow field propeller generated by Reynolds-averaged Navier–Stokes (RANS) and those Large Eddy Simulation (LES). The goal establishing is to generate LES data, which needs higher computing power requirements, with help RANS data. utilizes learning method computer vision handle three-dimensional LES. Firstly, samples are processed obtain their corresponding image respectively. Secondly, two kinds images used...

With the rapid development of AI algorithms and computational power, object recognition based on deep learning frameworks has become a major research direction in computer vision. UAVs equipped with detection systems are increasingly used fields like smart transportation, disaster warning, emergency rescue. However, due to factors such as environment, lighting, altitude, angle, UAV images face challenges small sizes, high density, significant background interference, making tasks difficult....