IET Collaborative Intelligent Manufacturing最新文献

RETRACTION: X. Wang, X. Zhang, H. Gong, J. Jiang, H. M. Rai: A flight control method for unmanned aerial vehicles based on vibration suppression. IET Collaborative Intelligent Manufacturing 3, no. 3, 252–261 (2021). https://doi.org/10.1049/cim2.12027.

The above article, published online on 26 March 2021 in Wiley Online Library (wileyonlinelibrary.com) has been retracted by agreement between the journal's Editors-in-Chief; Liang Gao and Weiming Shen; the Institution of Engineering and Technology; and John Wiley & Sons Ltd.

This article was published as part of a guest-edited special issue. Following an investigation, the IET, John Wiley & Sons Ltd and the journal have determined that the article was not reviewed in line with the journal's peer review standards and there is evidence that the peer review process of the corresponding special issue underwent systematic manipulation. In addition, the manuscript contains flaws and inconsistencies. Accordingly, we cannot vouch for the integrity or reliability of the content and have taken the decision to retract the article. The authors have been informed of the decision to retract.

RETRACTION: Y. Ma, Q. Chen, S. Wang, S. Sharma, S. Khanna: Research on dispersion compensation using avalanche photodiode and pin photodiode. IET Collaborative Intelligent Manufacturing 3, no. 3, 205–214 (2021). https://doi.org/10.1049/cim2.12000.

The above article, published online on 17 December 2020 in Wiley Online Library (wileyonlinelibrary.com) has been retracted by agreement between the journal's Editors-in-Chief; Liang Gao and Weiming Shen; the Institution of Engineering and Technology; and John Wiley & Sons Ltd.

This article was published as part of a guest-edited special issue. Following an investigation, the IET, John Wiley & Sons Ltd and the journal have determined that the article was not reviewed in line with the journal's peer review standards and there is evidence that the peer review process of the special issue underwent systematic manipulation. Furthermore, mistakes and inconsistencies were found in different figures of this manuscript. Accordingly, we cannot vouch for the integrity or reliability of the content and have taken the decision to retract the article. The authors have been informed of the decision to retract.

RETRACTION: Y. Xiao, Z. Jun, H. Lei, A. Sharma, A. Sharma: A novel method of material demand forecasting for power supply chains in industrial applications. IET Collaborative Intelligent Manufacturing 3, no. 3, 273–280 (2021). https://doi.org/10.1049/cim2.12007.

The above article, published online on 21 February 2021 in Wiley Online Library (wileyonlinelibrary.com) has been retracted by agreement between the journal's Editors-in-Chief; Liang Gao and Weiming Shen; the Institution of Engineering and Technology; and John Wiley & Sons Ltd.

This article was published as part of a guest-edited special issue. Following an investigation, the IET, John Wiley & Sons Ltd and the journal have determined that the article was not reviewed in line with the journal's peer review standards and there is evidence that the peer review process of the special issue underwent systematic manipulation. In addition, most graphs are missing relevant units and descriptors so that the results are not comprehensible. Accordingly, we cannot vouch for the integrity or reliability of the content and have taken the decision to retract the article. The authors have been informed and they disagree with the retraction.

RETRACTION: C. Ding, R. Kohli: Analysis of a building collaborative platform for Industry 4.0 based on Building Information Modelling technology. IET Collaborative Intelligent Manufacturing 3, no. 3, 233–242 (2021). https://doi.org/10.1049/cim2.12036.

The above article, published online on 21 August 2021 in Wiley Online Library (wileyonlinelibrary.com) has been retracted by agreement between the journal's Editors-in-Chief; Liang Gao and Weiming Shen; the Institution of Engineering and Technology; and John Wiley & Sons Ltd.

This article was published as part of a guest-edited special issue. Following an investigation, the IET, John Wiley & Sons Ltd and the journal have determined that the article was not reviewed in line with the journal's peer review standards and there is evidence that the peer review process of the special issue underwent systematic manipulation. Furthermore, the manuscript contains multiple inconsistencies and several scientific statements are not supported by relevant references. Accordingly, we cannot vouch for the integrity or reliability of the content and have taken the decision to retract the article. The authors have been informed of the decision to retract.

RETRACTION: P. R. Gabani, U. B. Gala, V. S. Narwane, R. D. Raut, U. H. Govindarajan, B. E. Narkhede: A viability study using conceptual models for last mile drone logistics operations in populated urban cities of India. IET Collaborative Intelligent Manufacturing 3, no. 3, 262–272 (2021). https://doi.org/10.1049/cim2.12006.

The above article, published online on 16 February 2021 in Wiley Online Library (wileyonlinelibrary.com) has been retracted by agreement between the journal's Editors-in-Chief; Liang Gao and Weiming Shen; the Institution of Engineering and Technology; and John Wiley & Sons Ltd.

This article was published as part of a guest-edited special issue. Following an investigation, the IET, John Wiley & Sons Ltd and the journal have determined that the article was not reviewed in line with the journal's peer review standards and there is evidence that the peer review process of the special issue underwent systematic manipulation. Furthermore, the conclusions of this manuscript are unsupported by any relevant experiments or calculations. Accordingly, we cannot vouch for the integrity or reliability of the content and have taken the decision to retract the article. The authors have been informed of the decision and disagree with the retraction.

RETRACTION: H. Qiang, M.A. Ikbal, S. Khanna: Prediction of energy consumption of numerical control machine tools and analysis of key energy-saving technologies. IET Collaborative Intelligent Manufacturing 3, no. 3, 215–223 (2021). https://doi.org/10.1049/cim2.12001.

The above article, published online on 1 February 2021 in Wiley Online Library (wileyonlinelibrary.com) has been retracted by agreement between the journal's Editors-in-Chief; Liang Gao and Weiming Shen; the Institution of Engineering and Technology; and John Wiley & Sons Ltd.

This article was published as part of a guest-edited special issue. Following an investigation, the IET, John Wiley & Sons Ltd and the journal have determined that the article was not reviewed in line with the journal's peer review standards and there is evidence that the peer review process of the corresponding special issue underwent systematic manipulation. Furthermore, the manuscript contains various logical flaws as well as unrelated references that do not support the scientific statements made. Accordingly, we cannot vouch for the integrity or reliability of the content and have taken the decision to retract the article. The authors have been informed of the decision to retract.

The development of multimodal large models and digital twin technology is set to revolutionise the methods of intelligent monitoring and maintenance for transformers. To address the issues of low intelligence level, single application mode, and poor human–machine collaboration in traditional transformer monitoring and maintenance methods, an intelligent monitoring and maintenance digital twin multimodal expert reasoning system, fine-tuned on visual language-based large models, is proposed. The paper explores the modes and methods for implementing intelligent monitoring and maintenance of transformers based on multimodal data, large models, and digital twin technology. A multimodal language large model (MLLM) framework for intelligent transformer maintenance, grounded on the Large Language and Vision Assistant model, has been designed. To enable large models to understand and reason about image annotation areas, an adaptive grid-based positional information processor has been designed. To facilitate the compatibility and learning of large models with transformer Dissolved Gas Analysis data, a heterogeneous modality converter based on the Gram–Schmidt angular field has been developed. For the unified modelling and management of multimodal reasoning and comprehensive resource integration in human–machine dialogue, a central linker based on an identity resolution asset management shell has been designed. Subsequently, a visual-language multimodal dataset for transformer monitoring and maintenance was constructed. Finally, by fine-tuning parameters, a multimodal expert reasoning system for intelligent transformer monitoring and maintenance was developed. This system not only achieves real-time monitoring of the transformer's operational status but also generates maintenance strategies intelligently based on operational conditions. The expert system possesses robust human–machine dialogue capabilities and reasoning generation abilities. This research provides a reference for the deep integration of MLLM and digital twin in industrial scenarios, particularly in the application modes of intelligent operation and maintenance for transformers.

RETRACTION: H. Sun, M. Fan, A. Sharma: Design and implementation of construction prediction and management platform based on building information modelling and three-dimensional simulation technology in Industry 4.0. IET Collaborative Intelligent Manufacturing 3, no. 3, 224–232 (2021). https://doi.org/10.1049/cim2.12019.

The above article, published online on 21st March 2021 in Wiley Online Library (wileyonlinelibrary.com) has been retracted by agreement between the journal's Editors-in-Chief; Liang Gao and Weiming Shen; the Institution of Engineering and Technology; and John Wiley and Sons Ltd.

This article was published as part of a guest-edited special issue. Following an investigation, the IET, John Wiley and Sons Ltd and the journal have determined that the article was not reviewed in line with the journal's peer review standards and there is evidence that the peer review process of the special issue underwent systematic manipulation. In addition, multiple inconsistencies and textual disconnections were found. As such, the research described is not comprehensible for readers. Accordingly, we cannot vouch for the integrity or reliability of the content and have taken the decision to retract the article. The authors have been informed and they disagree with the retraction.

Numerous files, such as records and logs, are generated in the process of equipment diagnosis and maintenance (D&M). These files contain lots of unstructured plain text. Knowledge in these files could be reused for similar equipment faults. In practice, knowledge presented in plain text is hard to acquire. Thus, automated named entity recognition (NER) and relation extraction (RE) methods based on pretrained encoders could be used to extract entities and relations and develop a structured knowledge graph (KG), thus facilitating intelligent manufacturing. However, equipment fault NER exhibits suboptimal performance with existing encoders pretrained on general-domain corpus. In this paper, domain-adaptation-based NER with information enrichment is proposed for developing an equipment fault KG. A domain-adapted encoder is tailored for equipment fault NER through domain-adaptive pretraining (DAPT). Update of word segmentation dictionary and adjustment of masking approach are implemented during DAPT for information enrichment, which helps make the most of the limited domain-specific pretraining corpus. Experimental results show that the F1 score of NER is improved by 1.22% using the domain-adapted encoder compared to its counterpart using the encoder pretrained on general-domain corpus. Furthermore, a reliable and robust question answering (QA) application of the developed equipment fault KG is also shown.

Robot perception is an important topic in artificial intelligence field, and tactile recognition in particular is indispensable for human–computer interaction. Efficiently classifying data obtained by touch sensors has long been an issue. In recent years, spiking neural networks (SNNs) have been widely used in tactile data categorisation due to their temporal information processing benefits, low power consumption, and high biological dependability. However, traditional SNN classification methods often encounter under-convergence when using membrane potential representation, decreasing their classification accuracy. Meanwhile, due to the time-discrete nature of SNN models, classification requires a significant time overhead, which restricts their real-time tactile sensing application potential. Considering these concerns, the authors propose a faster and more accurate SNN tactile classification approach using improved membrane potential representation. This method effectively overcomes model convergence problems by optimising the membrane potential expression and the relationship between the loss function and network parameters while significantly reducing the time overhead and enhancing the classification accuracy and robustness of the model. The experimental results show that the propose approach improves the classification accuracy by 4.16% and 2.71% and reduces the overall time by 8.00% and 8.14% on the EvTouch-Containers dataset and EvTouch-Objects dataset, respectively, when compared with existing models.