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Advances in AI Platform Infrastructure: Databases, Knowledge Management and Hardware Acceleration

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 November 2026 | Viewed by 1831

Special Issue Editors

Prof. Dr. Gang Wu

E-Mail Website
Guest Editor
School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China
Interests: multidisciplinary artificial intelligence; knowledge graphs; databases
Prof. Dr. Katarzyna Antosz

E-Mail Website
Guest Editor
Department of Manufacturing Processes and Production Engineering, Rzeszow University of Technology, 35-959 Rzeszów, Poland
Interests: industry 4.0; maintenance; production engineering; systems reliability; lean maintenance; decision support systems; artificial intelligence; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue aims to delve into cutting-edge advancements in AI platform infrastructure, with a specific focus on databases, knowledge management, and hardware acceleration. As artificial intelligence continues to evolve at a rapid pace, the robustness and efficiency of these underlying infrastructures are becoming increasingly pivotal for the performance, scalability, and intelligence of AI systems.

We invite contributions from researchers, academics, and industry professionals worldwide to share their latest findings, innovative technologies, and practical applications in these critical areas. The issue will explore how advancements in databases are enhancing data storage, retrieval, and processing capabilities for AI; how knowledge management is evolving to better organize, integrate, and utilize knowledge for AI applications; and how hardware acceleration is pushing the boundaries of AI performance through specialized processors and optimized architectures.

Topics of interest include, but are not limited to, the development of next-generation databases for AI, the integration of knowledge management systems to augment AI capabilities, and the design of hardware acceleration techniques to boost AI efficiency and scalability. We also encourage submissions that address the challenges, future trends, and potential breakthroughs in these domains, aiming to provide a comprehensive overview of the current state and future directions of AI platform infrastructure.

Prof. Dr. Gang Wu
Prof. Dr. Katarzyna Antosz
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • AI platform infrastructure
  • databases for AI
  • knowledge management
  • hardware acceleration
  • scalability and performance in AI systems

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Published Papers (3 papers)

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Research

21 pages, 6274 KB  
Article
Discriminative Representation Learning for Fast and Accurate Clustering
by Haiwei Hou and Lijuan Wang
Appl. Sci. 2026, 16(6), 2887; https://doi.org/10.3390/app16062887 - 17 Mar 2026
Viewed by 313
Abstract
Deep clustering aims to boost clustering performance by learning powerful representations via deep learning. Despite their superiority over conventional shallow algorithms, autoencoder-based methods are typically hindered by heavy dependencies on large datasets and computationally expensive pre-training phases. Moreover, they often struggle to learn [...] Read more.
Deep clustering aims to boost clustering performance by learning powerful representations via deep learning. Despite their superiority over conventional shallow algorithms, autoencoder-based methods are typically hindered by heavy dependencies on large datasets and computationally expensive pre-training phases. Moreover, they often struggle to learn representations that are sufficiently discriminative for complex clustering tasks. To bridge this gap, we introduce a novel discriminative clustering framework utilizing Siamese encoders. By jointly training a Siamese encoder and a discriminative learning module, our method simultaneously captures robust features from data augmentations and imposes intra-cluster compactness. This dual optimization yields highly discriminative representations, which obviates the necessity for pre-training while ensuring rapid convergence and high accuracy. Extensive experiments on multiple benchmarks validate the superiority of our approach over state-of-the-art baselines. Full article
(This article belongs to the Special Issue Advances in AI Platform Infrastructure: Databases, Knowledge Management and Hardware Acceleration)
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24 pages, 2078 KB  
Article
A Few-Shot Bearing Fault Diagnosis Method Integrating Improved Generative Adversarial Network and CNN-BiLSTM-Attention Hybrid Network
by Shiqun Liu, Xingli Liu and Zhaoyong Jiang
Appl. Sci. 2026, 16(6), 2660; https://doi.org/10.3390/app16062660 - 11 Mar 2026
Viewed by 454
Abstract
Artificial intelligence technology offers an intelligent and efficient new pathway for bearing fault diagnosis, holding significant importance for ensuring the stable operation of industrial systems. However, bearing fault samples are scarce in industrial practice, and traditional data-driven methods exhibit a marked decline in [...] Read more.
Artificial intelligence technology offers an intelligent and efficient new pathway for bearing fault diagnosis, holding significant importance for ensuring the stable operation of industrial systems. However, bearing fault samples are scarce in industrial practice, and traditional data-driven methods exhibit a marked decline in diagnostic performance under conditions of small sample sizes. To address this, this paper proposes a few-shot bearing fault diagnosis method that integrates an Improved Generative Adversarial Network with a CNN-BiLSTM-Attention hybrid network. The method comprises three core stages: in the data augmentation stage, a class-center-constrained Least Squares Generative Adversarial Network (CCC-LSGAN) model featuring class center constraint and joint loss optimization is proposed to generate high-quality fault samples through frequency-domain feature constraints, effectively expanding the training data; in the feature learning stage, a one-dimensional Convolutional Neural Network, Bidirectional Long Short-Term Memory, and Attention hybrid network (1D-CNN-BiLSTM-Attention) hybrid base classifier is constructed, which combines multi-scale convolution, bidirectional temporal modeling, and attention mechanisms to fully extract the spatiotemporal features of vibration signals; in the inference stage, test-time noise augmentation and a multi-model weighted voting ensemble mechanism are introduced to enhance the robustness and generalization capability of the diagnosis. Experimental results based on the PU and CWRU public bearing datasets demonstrate that the proposed method significantly outperforms existing mainstream diagnostic approaches in core metrics, including accuracy, precision, recall, and F1 score. It achieves a diagnostic accuracy of 96.60% on the PU dataset and 98.58% on the CWRU dataset. This method verifies the feasibility of highly reliable diagnosis under few-shot conditions and provides an effective solution for the intelligent operation and maintenance of industrial equipment. Full article
(This article belongs to the Special Issue Advances in AI Platform Infrastructure: Databases, Knowledge Management and Hardware Acceleration)
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23 pages, 3488 KB  
Article
Building and Validating a Coal Mine Safety Question-Answering System with a Large Language Model Through a Two-Stage Fine-Tuning Method
by Zongyu Li, Xingli Liu, Shiqun Liu, He Ma and Gang Wu
Appl. Sci. 2026, 16(2), 971; https://doi.org/10.3390/app16020971 - 17 Jan 2026
Viewed by 419
Abstract
Artificial intelligence technology holds significant importance for building intelligent question-answering systems in the field of coal mine safety and enhancing safety management levels. Currently, there is a lack of specialized large language models and high-quality question-answering datasets in this field. To address this, [...] Read more.
Artificial intelligence technology holds significant importance for building intelligent question-answering systems in the field of coal mine safety and enhancing safety management levels. Currently, there is a lack of specialized large language models and high-quality question-answering datasets in this field. To address this, this study proposes a two-stage fine-tuning method based on Low-Rank Adaptation (LoRA) and Group Sequence Policy Optimization (GSPO) for training a question-answering model tailored to the coal mine safety domain. The research begins by constructing a dedicated question-answering dataset based on domain-specific regulatory documents. Subsequently, using Qwen2.5-7B Instruct as the base model, the study fine-tunes the model through supervised learning with LoRA technology, followed by further optimization of the model’s performance using the GSPO reinforcement learning algorithm. Experiments show that the model trained with this method exhibits significant improvements in coal mine safety-related tasks, achieving superior results on multiple automated evaluation metrics compared to contrast models of similar scale. This study validates the effectiveness of the two-stage fine-tuning method in adapting large language models (LLMs) to specific domains, providing a new technical approach for the intelligentization of coal mine safety. It should be noted that due to the lack of external data, this study relies on a self-constructed dataset and has not yet undergone external independent validation, which constitutes the main limitation of the current work. Full article
(This article belongs to the Special Issue Advances in AI Platform Infrastructure: Databases, Knowledge Management and Hardware Acceleration)
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