Coating matching recommendation based on improved fuzzy comprehensive evaluation and collaborative filtering algorithm

Yuan, Xin; Bu, Henan; Niu, Jianmin; Yu, Wei; Zhou, Honggen; Ji, Xingyu; Pengfei, Ye

doi:10.1038/s41598-021-93628-4

Cited by 12 publications

(14 citation statements)

References 37 publications

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“…The outer plate above the waterline is mainly corroded by the marine atmosphere, sea spray, rain and snow, seawater used for washing the deck and condensation water. To prevent corrosion, the corresponding coating matching for this part is shown in Table 2 [2]. At present, the painting equipment that can be used for the outer plate above the waterline in the shipyard mainly includes side suspension painting robots, rail painting robots, high-pressure airless sprayers, internal rail painting robots.…”

Section: Experimental Validation and Results Analysismentioning

confidence: 99%

“…Ship painting is one of the three pillars of modern shipbuilding and is used throughout ship construction [1]. The design of the ship painting process mainly includes the selection of coating matching, the development of surface treatment level, the development of secondary descaling grade and the design of the process routine [2]. A scientific and reasonable painting process is the key to ensure the quality of the ship's construction.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, the paintings used in different parts need to have different anti-corrosion performance requirements. The diversity of paintings determines the construction conditions, the construction process and the painting equipment [2]. Therefore, a series of process routes, process methods need to be formulated.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Ship Painting Process Design Based on IDBSACN-RF

Yuan

Niu

et al. 2021

Coatings

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The painting process is an essential part of the shipbuilding process. Its quality is directly related to the service life and maintenance cost of the ship. Currently, the design of the painting process relies on the experience of technologists. It is not conducive to scientific management of the painting process and effective control of painting cost. Therefore, an intelligent design algorithm for the ship painting process is proposed in this paper. Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is used to form categories of painting objects by cluster analysis. The grey wolf optimization (GWO) is introduced to realize the adaptive determination of clustering parameters and avoid the deviation of clustering results. Then, a painting object classification model is constructed based on the random forest (RF). Finally, the recommendation of the painting process is realized based on the multi-objective evaluation function. Effectiveness is verified by taking the outer plate above the waterline of a shipyard H1127/7 as the object. The results show that the performance of DBSCAN is significantly improved. Furthermore, the accurate classification of painting objects by RF is achieved. The experiment proves that the dry film thickness qualification rate obtained by the painting process designed by IDBSCAN-RF is 92.3%, which meets the requirements of the performance standard of protective coatings (PSPC).

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Section: Experimental Validation and Results Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Ship Painting Process Design Based on IDBSACN-RF

Yuan

Niu

et al. 2021

Coatings

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show abstract

“…Commonly used calculation formulas are such as modified cosine similarity, Pearson, Jaccard similarity, etc. Through similar user groups, some potentially preferred content will also be recommended to the target customer, reflecting the diversity of recommendation results of collaborative filtering [15][16]. As the data expands, the likelihood of different users being interested in the same content among themselves gradually decreases, and the problem of data sparsity cannot be avoided.…”

Section: A Recommendation Algorithms Based On Own Preference Modelsmentioning

confidence: 99%

Design of a Hybrid Recommendation Algorithm based on Multi-objective Collaborative Filtering for Massive Cloud Data

Zhou¹

2023

IJACSA

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The current recommendation technology has some problems, such as lack of timeliness, the contradiction between recommendation diversity and accuracy. In order to solve the problem of lack of timeliness, the time factor is introduced when constructing the self-preference model. The cold start problem in the collaborative filtering algorithm is solved by the hybrid similarity calculation method, and the potential preference model is constructed. The two are fused to obtain a hybrid recommendation algorithm to improve the recommendation performance of the algorithm. For the problem of multi-objective contradiction, the NNIA algorithm is used to further optimize the candidate results of mixed recommendation, and the final recommendation list is obtained. Through verification experiments, the results show that the recall rate and accuracy of the fused preference model are better than those of the non-fused model, and the accuracy is 9.57% and 8.23% higher than that of SPM and PPM, and the recall rate is 9.97% and 7.65% higher, respectively. CBCF-NNIA algorithm has high accuracy and diversity of recommendation, and can provide users with rich and diverse text content to meet their own needs.

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“…Ship hull surface coating can effectively prevent all parts of the ship from being corroded by the atmosphere and seawater. Still, it also has anti-fouling, beautification, decoration, and other special functions [2]. The coating quality is directly related to the ship's construction cycle and maintenance costs, as well as an essential factor affecting the hull's corrosion resistance and the ship's service life [3].…”

Section: Introductionmentioning

confidence: 99%

A Zero-Shot Image Classification Method of Ship Coating Defects Based on IDATLWGAN

Bu,

Yang,

et al. 2024

Coatings

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In recent years, the defect image classification method based on deep transfer learning has been widely explored and researched, and the task of source and target domains with the same painting defect image class has been solved successfully. However, in real applications, due to the complexity and uncertainty of ship painting conditions, it is very likely that there are unknown classes of painting defects, and the traditional deep learning model cannot identify a few classes, which leads to model overfitting and reduces its generalization ability. In this paper, a zero-shot Image classification method for ship painting defects based on IDATLWGAN is proposed to identify new unknown classes of defects in the target domain. The method is based on a deep convolutional neural network combined with adversarial transfer learning. First, a preprocessed ship painting defect dataset is used as input for the domain-invariant feature extractor. Then, the domain invariant feature extractor takes domain invariant features from the source and target domains. Finally, Defect discriminators and domain alignment discriminators are employed to classify the known categories of unlabeled defects and unknown categories of unlabeled defects in the target domain and to further reduce the distance between the edge distributions of the source and target domains. The experimental results show that the proposed model in this paper extracts a better distribution of invariant features in the source and target domains compared to other existing transfer learning models. It can successfully complete the migration task and accurately recognize the painting defects of known categories and new unknown categories, which is a perfect combination of intelligent algorithms and engineering practice.

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Coating matching recommendation based on improved fuzzy comprehensive evaluation and collaborative filtering algorithm

Cited by 12 publications

References 37 publications

Ship Painting Process Design Based on IDBSACN-RF

Ship Painting Process Design Based on IDBSACN-RF

Design of a Hybrid Recommendation Algorithm based on Multi-objective Collaborative Filtering for Massive Cloud Data

A Zero-Shot Image Classification Method of Ship Coating Defects Based on IDATLWGAN

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