Advances in Engineering Innovation
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Advances in Engineering Innovation (AEI) is a peer-reviewed, fast-indexing open access journal co-published by EWA Publishing and Tianjin University Research Centre on Data Intelligence and Cloud-Edge-Client Service Engineering. AEI is a comprehensive journal focusing on multidisciplinary areas of engineering and at the interface of related subjects, including, but not limited to, Artificial Intelligence, Biomedical Engineering, Electrical and Electronic Engineering, Materials Engineering, Traffic and Transportation Engineering, etc. For the details about the journal's scope, please refer to the Aims and Scope page. For more information about the journal, please refer to the FAQ page or contact info@ewapublishing.org.

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July 21, 2023

Advances in Engineering Innovation - Gender and Diversity pledge


We pledge to our journal community:

  • We're committed: we put diversity and inclusion at the heart of our activities
  • We champion change: we're working to increase the percentage of women, early career ...

July 6, 2023

Advances in Engineering Innovation - Disclaimer


  • The statements, opinions and data contained in the journal Advances in Engineering Innovation (AEI) are solely those of the individual authors and contributors and not of the publisher and the editor(s). EWA Publishing stays neutral with regard to jurisdictional claims in published maps and ...
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    Latest Articles

    Open Access | Article

    As a leading industry of low-altitude economy and an emphasis area of consumer goods trade-in, in recent years, the Unmanned Aircraft System (UAS) industry has successively received the attention of the Standardization Administration of the P.R.C, the State Administration for Market Regulation and other departments, which have all emphasized the importance of giving full play to the leading role of standards. Up to now, the construction of China's UAS standard system has made great progress, and 126 key standards that are urgently needed by the market and support supervision have been formulated and revised in a timely manner, but the following problems still exist: the standard terminology is not yet unified, the way of prioritizing the formulation and revision is too simple, the content of the standard system is still imperfect, and there is a lack of a unified public service platform for standard information. In order to solve these problems, this paper combines the new international standardization construction experience of the European, the United States and other countries, and puts forward the corresponding optimization ideas for the construction of the UAS standard system, so as to promote the healthy and orderly development of the UAS industry.

    Open Access | Article

    This project aims to construct a knowledge graph system applied to the field of traditional Chinese medicine (TCM) by extracting entities (such as drugs, diseases, etc.) and their relationships from TCM medical case data and storing them in a Neo4j database. The project process includes data reading, entity recognition and extraction, data formatting, and data import into the database. The project not only improved the individual's proficiency in Python data processing techniques (including regular expressions and JSON parsing) but also enhanced their skills in knowledge graph construction and database operations. In the future, there is a desire to further improve technical capabilities, explore more cutting-edge technologies in the TCM field, and promote project progress through collaboration, contributing to the modernization of TCM and intelligent healthcare services.

    Open Access | Article

    This paper conducts research on data intelligent generation and analysis based on the ChatGPT model. To explore ChatGPT's performance and limitations in machine translation tasks, the concepts of the Transformer model and previous studies were reviewed to gain a deep understanding of the principles and roles of components such as attention mechanisms, encoders, decoders, and word embeddings. By controlling ChatGPT through code for machine translation and performing manual verification, the model's limitations in handling synonyms, technical terms, and specific domain languages were identified.

    Open Access | Article

    This paper aims to design a news recommendation model based on user preferences to address the issues in recommendation systems under large datasets. Initially, four datasets—click_history, news, news_embedding, and user_predict—were integrated into a single table, followed by data cleaning and feature engineering. Due to the large volume of data, this paper proposes necessary data filtering for the training and testing sets, utilizing temporal data to construct user feature vectors and news feature vectors. One challenge is how to effectively integrate user preferences and news features into the model to avoid overfitting or underfitting. In the model design and building phase, different methods were attempted to merge the information of users and news. Ultimately, the user preference features were processed using a fully connected layer, and the news embedding vectors were handled using an LSTM model. These two data parts were then combined into another fully connected layer, using ReLU as the activation function and CELoss as the loss function. Subsequently, the model's hyperparameters were adjusted and evaluated, achieving favorable model performance. The prediction accuracy for recommending news to users in user_predict was calculated as an evaluation criterion. Finally, this paper proposes directions for generalization and optimization in three aspects: data processing, model design, and experimental design. This includes data processing methods, potential improvements or mechanisms that could be incorporated into the model, and hyperparameter tuning. The paper primarily proposes data filtering to solve the problem of excessive data scale, which may aid in addressing recommendation system issues under large-scale datasets.

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