A Flight Arrival Time Prediction Method Based on Cluster Clustering-Based Modular With Deep Neural Network

Deng, Wu; Li, Kunpeng; Zhao, Huimin

doi:10.1109/tits.2023.3338251

Cited by 19 publications

(3 citation statements)

References 35 publications

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“…Traditional machine vision-based detection methods usually involve two main steps: feature extraction and classification. These methods first extract features such as texture 1 and spectrum 2 – 4 and then employ classifiers like SVM 5 , ELM 6 , or clustering 7 – 9 to perform detection tasks. However, these traditional methods heavily rely on manually designed algorithms 10 and are mostly suitable for specific types of defects, so they suffer from poor robustness and generalization.…”

Section: Introductionmentioning

confidence: 99%

Dual-branch information extraction and local attention anchor-free network for defect detection

Wang,

Zhang,

Chen

2024

Sci Rep

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In the production process, the presence of surface defects seriously affects the quality of industrial products. Existing defect detectors are not suitable for surface with scattered distribution and complex texture of defects. In this study, a dual-branch information extraction and local attention anchor-free network for defect detection (DLA-FCOS), which is based on the fully convolutional one-stage network, is proposed to accurately locate and detect surface defects of industrial products. Firstly, a dual-branch feature extraction network (DFENeT) is proposed and used to improve the extraction ability of complex defects. Then, a local feature enhancement module is proposed, and a residual connection is established to enrich local semantic information. Meanwhile, the self-attention mechanism is introduced to form local attentional residual feature pyramid networks (LA-RFPN) to eliminate the influences of feature misalignments. The mean average accuracy (mAP) and frames per second (FPS) of the proposed DLA-FCOS on the cut layer of the tobacco packet defect dataset (CLTP-DD) are 96.8% and 20.7, respectively, which meets the requirements for accurate and real-time defect detection. Meanwhile, the average accuracy of the proposed DLA-FCOS on the NEU-DET and GC10-DET datasets is 78.4% and 67.7%, respectively. The results demonstrate that the DLA-FCOS has good feasibility and high generalization capability to perform defect detection tasks of industrial products.

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Section: Introductionmentioning

confidence: 99%

Dual-branch information extraction and local attention anchor-free network for defect detection

Wang,

Zhang,

Chen

2024

Sci Rep

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“…Most existing optimization algorithms have many parameters, tend to fall into local optimality and cannot solve complex optimization problems. As a result, newly proposed algorithms are increasing, such as greylag goose optimization (GGO) [28], Coati optimization algorithm (COA) [29], parrot optimizer (PO) [30], reptile search algorithm (RSA) [31], MOQEA/D [32], machine learning (ML) [33], deep neural network (DNN) [34], image segmentation [35] and so on [36]. In addition, there are also winners of CEC competitions such as LSHADE [37], COLSHADE [38], IMODE [39], KGE [40] and SASS [41].…”

Section: Introductionmentioning

confidence: 99%

Enhanced Wild Horse Optimizer with Cauchy Mutation and Dynamic Random Search for Hyperspectral Image Band Selection

Chen,

Sun,

Chen

et al. 2024

Electronics

Self Cite

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The high dimensionality of hyperspectral images (HSIs) brings significant redundancy to data processing. Band selection (BS) is one of the most commonly used dimensionality reduction (DR) techniques, which eliminates redundant information between bands while retaining a subset of bands with a high information content and low noise. The wild horse optimizer (WHO) is a novel metaheuristic algorithm widely used for its efficient search performance, yet it tends to become trapped in local optima during later iterations. To address these issues, an enhanced wild horse optimizer (IBSWHO) is proposed for HSI band selection in this paper. IBSWHO utilizes Sobol sequences to initialize the population, thereby increasing population diversity. It incorporates Cauchy mutation to perturb the population with a certain probability, enhancing the global search capability and avoiding local optima. Additionally, dynamic random search techniques are introduced to improve the algorithm search efficiency and expand the search space. The convergence of IBSWHO is verified on commonly used nonlinear test functions and compared with state-of-the-art optimization algorithms. Finally, experiments on three classic HSI datasets are conducted for HSI classification. The experimental results demonstrate that the band subset selected by IBSWHO achieves the best classification accuracy compared to conventional and state-of-the-art band selection methods, confirming the superiority of the proposed BS method.

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“…The traditional methods difficultly solve these problems [4][5][6]. In recent years, some optimization methods are presented, such as particle swarm optimization (PSO) [7][8][9][10][11], ant colony optimization (ACO) [12][13][14], moth flame optimization(MFO) [15][16][17], grey wolf optimizer (GWO) [18][19][20][21], and so on [22][23][24][25][26]. Although these algorithms can get better solutions in solving these problems than traditional methods, they still have lower accuracy and high time complexity.…”

Section: Introductionmentioning

confidence: 99%

Artificial bee colony optimization algorithm with full dimensional updating strategy and its application

Li,

Deng

2024

Phys. Scr.

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For the low accuracy and slow convergence speed of artificial bee colony(ABC) algorithm in solving complex optimization problems, an improved artificial bee colony(ABC) algorithm based on the new full dimensional updating ABC/best /1 strategy, namely FNABC was proposed in this paper. In the FNABC, for the low efficiency of one-dimensional search, the full dimensional updating search strategy and ABC/best /1 strategy were combined to design a new full dimensional updating ABC/best/1 strategy, which expanded the search space, improved the mining ability and search efficiency. Then, a new evolutionary phase is designed to balance the global search ability and local mining ability to avoid falling into local optimum and improve the convergence accuracy. Finally, the FNABC is compared with eight state-of-the-art ABC variants such as AABC, iqABC, MEABC, ABCVSS, GBABC, DFSABC, MABC-NS, MGABC in solving 12 complex functions. All functions have obtained the best optimal values among 9 algorithms. Additionally, FNABC is applied to solve a real-world train operation adjustment problem. The experiment results indicate that the FNABC has better optimization ability, scalability and robustness. It obtains the ideal train operation adjustment results.

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A Flight Arrival Time Prediction Method Based on Cluster Clustering-Based Modular With Deep Neural Network

Cited by 19 publications

References 35 publications

Dual-branch information extraction and local attention anchor-free network for defect detection

Dual-branch information extraction and local attention anchor-free network for defect detection

Enhanced Wild Horse Optimizer with Cauchy Mutation and Dynamic Random Search for Hyperspectral Image Band Selection

Artificial bee colony optimization algorithm with full dimensional updating strategy and its application

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