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Electronics
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Machine Intelligent Information and Efficient System
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Machine Intelligent Information and Efficient System

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Systems & Control Engineering".

Deadline for manuscript submissions: 15 May 2025 | Viewed by 10091

Special Issue Editors

Dr. Zhiwei Liu

E-Mail Website
Guest Editor
Salesforce AI Research, Palo Alto, CA, USA
Interests: information systems; data mining; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Li Sun

E-Mail Website1 Website2
Guest Editor
School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China
Interests: data mining; machine learning; social network analysis
Dr. Renyu Yang

E-Mail Website
Guest Editor
School of Computing, Faculty of Engineering and Physical Sciences, University of Leeds, Leeds LS2 9JT, UK
Interests: large-scale distributed systems; resource management; fault tolerance and software reliability; big data processing and analytic; applied machine learning (graph representation learning); reinforcement learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent years have witnessed the booming of machine intelligent systems, from online information systems to on-device hardware systems. Advanced machine intelligent systems are able to automatically collect data, analyze their patterns, and yield correct predictions and operations. In particular, the development of AI techniques have accelerated the research into machine intelligent systems, from large-scale deep neural networks to lightweight on-device models.

While significant progress has been made in the field of machine intelligence, there are still numerous limitations and challenges to achieving the full potential of these systems. One of the main challenges is the overwhelming amount of information that needs to be processed and analyzed for intelligent decision making. This is particularly intricate for large-scale information systems, where large amounts of data can be exploited for accurate prediction and recommendation. There is also a huge requirement for efficient systems that can operate in real-time and dynamic environments and handle complex tasks at scale. It is, therefore, imperative to devise efficient and resilient algorithms and architectures.

This Special Issue is dedicated to advancements in the field of machine intelligent systems, with a special focus on efficiency, performance, and resilience. Recent insights into the underlying mechanisms have paved the way for a plethora of novel applications in a wide range of fields, from learning models and paradigms to system frameworks and deployment infrastructures, to name a few. We welcome any original research articles and reviews related to such fields. Research areas may include (but are not limited to) the following:

  • Intelligent IoT and IIoT;
  • Intelligent Recommender systems;
  • Intelligent Language Systems;
  • Intelligent Visual Processing Systems;
  • Intelligent Event Detection Systems;
  • Intelligent blockchain systems;
  • Intelligent Education Systems;
  • Intelligent Industrial Control and Decision System;
  • Large-scale data and knowledge systems;
  • Multimedia systems;
  • Smart Wearable Systems;
  • Smart Healthcare Systems;
  • Smart Transportation Systems;
  • Trustworthy in Intelligent Systems;
  • Reasoning in Intelligent Systems.

Dr. Zhiwei Liu
Dr. Li Sun
Dr. Renyu Yang
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 100 words) can be sent to the Editorial Office for announcement on this website.

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. Electronics 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

  • machine intelligent systems
  • information systems
  • efficient systems

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Published Papers (6 papers)

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Research

27 pages, 4362 KiB  
Article
Enhancing Resource Utilization Efficiency in Serverless Education: A Stateful Approach with Rofuse
by Xinxi Lu, Nan Li, Lijuan Yuan and Juan Zhang
Electronics 2024, 13(11), 2168; https://doi.org/10.3390/electronics13112168 - 2 Jun 2024
Viewed by 538
Abstract
Traditional container orchestration platforms often suffer from resource wastage in educational settings, and stateless serverless services face challenges in maintaining container state persistence during the teaching process. To address these issues, we propose a stateful serverless mechanism based on Containerd and Kubernetes, focusing [...] Read more.
Traditional container orchestration platforms often suffer from resource wastage in educational settings, and stateless serverless services face challenges in maintaining container state persistence during the teaching process. To address these issues, we propose a stateful serverless mechanism based on Containerd and Kubernetes, focusing on optimizing the startup process for container groups. We first implement a checkpoint/restore framework for container states, providing fundamental support for managing stateful containers. Building on this foundation, we propose the concept of “container groups” to address the challenges in educational practice scenarios characterized by a large number of similar containers on the same node. We then propose the Rofuse optimization mechanism, which employs delayed loading and block-level deduplication techniques. This enables containers within the same group to reuse locally cached file system data at the block level, thus reducing container restart latency. Experimental results demonstrate that our stateful serverless mechanism can run smoothly in typical educational practice scenarios, and Rofuse reduces the container restart time by approximately 50% compared to existing solutions. This research provides valuable exploration for serverless practices in the education domain, contributing new perspectives and methods to improve resource utilization efficiency and flexibility in teaching environments. Full article
(This article belongs to the Special Issue Machine Intelligent Information and Efficient System)
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15 pages, 862 KiB  
Article
GraM: Geometric Structure Embedding into Attention Mechanisms for 3D Point Cloud Registration
by Pin Liu, Lin Zhong, Rui Wang, Jianyong Zhu, Xiang Zhai and Juan Zhang
Electronics 2024, 13(10), 1995; https://doi.org/10.3390/electronics13101995 - 20 May 2024
Viewed by 1020
Abstract
3D point cloud registration is a crucial technology for 3D scene reconstruction and has been successfully applied in various domains, such as smart healthcare and intelligent transportation. With theoretical analysis, we find that geometric structural relationships are essential for 3D point cloud registration. [...] Read more.
3D point cloud registration is a crucial technology for 3D scene reconstruction and has been successfully applied in various domains, such as smart healthcare and intelligent transportation. With theoretical analysis, we find that geometric structural relationships are essential for 3D point cloud registration. The 3D point cloud registration method achieves excellent performance only when fusing local and global features with geometric structure information. Based on these discoveries, we propose a 3D point cloud registration method based on geometric structure embedding into the attention mechanism (GraM), which can extract the local features of the non-critical point and global features of the corresponding point containing geometric structure information. According to the local and global features, the simple regression operation can obtain the transformation matrix of point cloud pairs, thereby eliminating the semantics that ignores the geometric structure relationship. GraM surpasses the state-of-the-art results by 0.548° and 0.915° regarding the relative rotation error on ModelNet40 and LowModelNet40, respectively. Full article
(This article belongs to the Special Issue Machine Intelligent Information and Efficient System)
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17 pages, 1173 KiB  
Article
PHIR: A Platform Solution of Data-Driven Health Monitoring for Industrial Robots
by Fei Jiang, Chengyun Hu, Chongwei Liu, Rui Wang, Jianyong Zhu, Shiru Chen and Juan Zhang
Electronics 2024, 13(5), 834; https://doi.org/10.3390/electronics13050834 - 21 Feb 2024
Viewed by 1149
Abstract
The large-scale application of industrial robots has created a demand for more intelligent and efficient health monitoring, which is more efficiently met by data-driven methods due to the surge in data and the advancement of computing technology. However, applying deep learning methods to [...] Read more.
The large-scale application of industrial robots has created a demand for more intelligent and efficient health monitoring, which is more efficiently met by data-driven methods due to the surge in data and the advancement of computing technology. However, applying deep learning methods to industrial robots presents critical challenges such as data collection, application packaging, and the need for customized algorithms. To overcome these difficulties, this paper introduces a Platform of data-driven Health monitoring for IRs (PHIR) that provides a universal framework for manufacturers to utilize deep-learning-based approaches with minimal coding. Real-time data from multiple IRs and sensors is collected through a cloud-edge system and undergoes unified pre-processing to facilitate model training with a large volume of data. To enable code-free development, containerization technology is used to convert algorithms into operators, and users are provided with a process orchestration interface. Furthermore, algorithm research both for sudden fault and long-term aging failure detection is conducted and applied to the platform for industrial robot health monitoring experiments, by which the superiority of the proposed platform, in reality, is proven through positive results. Full article
(This article belongs to the Special Issue Machine Intelligent Information and Efficient System)
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16 pages, 7935 KiB  
Article
Machine Fault Diagnosis through Vibration Analysis: Continuous Wavelet Transform with Complex Morlet Wavelet and Time–Frequency RGB Image Recognition via Convolutional Neural Network
by Dominik Łuczak
Electronics 2024, 13(2), 452; https://doi.org/10.3390/electronics13020452 - 22 Jan 2024
Cited by 14 | Viewed by 2696
Abstract
In pursuit of advancing fault diagnosis in electromechanical systems, this research focusses on vibration analysis through innovative techniques. The study unfolds in a structured manner, beginning with an introduction that situates the research question in a broader context, emphasising the critical role of [...] Read more.
In pursuit of advancing fault diagnosis in electromechanical systems, this research focusses on vibration analysis through innovative techniques. The study unfolds in a structured manner, beginning with an introduction that situates the research question in a broader context, emphasising the critical role of fault diagnosis. Subsequently, the methods section offers a concise summary of the primary techniques employed, highlighting the utilisation of short-time Fourier transform (STFT) and continuous wavelet transform (CWT) for extracting time–frequency components from the signal. The results section succinctly summarises the main findings of the article, showcasing the results of features extraction by CWT and subsequently utilising a convolutional neural network (CNN) for fault diagnosis. The proposed method, named CWTx6-CNN, was compared with the STFTx6-CNN method of the previous stage of the investigation. Visual insights into the time–frequency characteristics of the inertial measurement unit (IMU) data are presented for various operational classes, offering a clear representation of fault-related features. Finally, the conclusion section underscores the advantages of the suggested method, particularly the concentration of single-frequency components for enhanced fault representation. The research demonstrates commendable classification performance, highlighting the efficiency of the suggested approach in real-time scenarios of fault analysis in less than 50 ms. Calculation by CWT with a complex Morlet wavelet of six time–frequency images and combining them into a single colour image took less than 35 ms. In this study, interpretability techniques have been employed to address the imperative need for transparency in intricate neural network models, particularly in the context of the case presented. Notably, techniques such as Grad-CAM (gradient-weighted class activation mapping), occlusion, and LIME (locally interpretable model-agnostic explanation) have proven instrumental in elucidating the inner workings of the model. Through a comparative analysis of the proposed CWTx6-CNN method and the reference STFTx6-CNN method, the application of interpretability techniques, including Grad-CAM, occlusion, and LIME, has played a pivotal role in revealing the distinctive spectral representations of these methodologies. Full article
(This article belongs to the Special Issue Machine Intelligent Information and Efficient System)
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21 pages, 6781 KiB  
Article
Prototype-Based Support Example Miner and Triplet Loss for Deep Metric Learning
by Shan Yang, Yongfei Zhang, Qinghua Zhao, Yanglin Pu and Hangyuan Yang
Electronics 2023, 12(15), 3315; https://doi.org/10.3390/electronics12153315 - 2 Aug 2023
Cited by 3 | Viewed by 1524
Abstract
Deep metric learning aims to learn a mapping function that projects input data into a high-dimensional embedding space, facilitating the clustering of similar data points while ensuring dissimilar ones are far apart. The most recent studies focus on designing a batch sampler and [...] Read more.
Deep metric learning aims to learn a mapping function that projects input data into a high-dimensional embedding space, facilitating the clustering of similar data points while ensuring dissimilar ones are far apart. The most recent studies focus on designing a batch sampler and mining online triplets to achieve this purpose. Conventionally, hard negative mining schemes serve as the preferred batch sampler. However, most hard negative mining schemes search for hard examples in randomly selected mini-batches at each epoch, which often results in less-optimal hard examples and thus sub-optimal performances. Furthermore, Triplet Loss is commonly adopted to perform online triplet mining by pulling the hard positives close to and pushing the negatives away from the anchor. However, when the anchor in a triplet is an outlier, the positive example will be pulled away from the centroid of the cluster, thus resulting in a loose cluster and inferior performance. To address the above challenges, we propose the Prototype-based Support Example Miner (pSEM) and Triplet Loss (pTriplet Loss). First, we present a support example miner designed to mine the support classes on the prototype-based nearest neighbor graph of classes. Following this, we locate the support examples by searching for instances at the intersection between clusters of these support classes. Second, we develop a variant of Triplet Loss, referred to as a Prototype-based Triplet Loss. In our approach, a dynamically updated prototype is used to rectify outlier anchors, thus reducing their detrimental effects and facilitating a more robust formulation for Triplet Loss. Extensive experiments on typical Computer Vision (CV) and Natural Language Processing (NLP) tasks, namely person re-identification and few-shot relation extraction, demonstrated the effectiveness and generalizability of the proposed scheme, which consistently outperforms the state-of-the-art models. Full article
(This article belongs to the Special Issue Machine Intelligent Information and Efficient System)
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12 pages, 545 KiB  
Article
Research on Fraud Detection Method Based on Heterogeneous Graph Representation Learning
by Xuxu Zheng, Chen Feng, Zhiyi Yin, Jinli Zhang and Huawei Shen
Electronics 2023, 12(14), 3070; https://doi.org/10.3390/electronics12143070 - 14 Jul 2023
Cited by 1 | Viewed by 1739
Abstract
Detecting fraudulent users in social networks could reduce online fraud and telecommunication fraud cases, which is essential to protect the lives and properties of internet users and maintain social harmony and stability. We study how to detect fraudulent users by using heterogeneous graph [...] Read more.
Detecting fraudulent users in social networks could reduce online fraud and telecommunication fraud cases, which is essential to protect the lives and properties of internet users and maintain social harmony and stability. We study how to detect fraudulent users by using heterogeneous graph representation learning and propose a heterogeneous graph representation learning algorithm to learn user node embeddings to reduce human intervention. The experimental results show promising results. This article investigates how to use better heterogeneous graph representation learning to detect fraudulent users in social networks and improve detection accuracy. Full article
(This article belongs to the Special Issue Machine Intelligent Information and Efficient System)
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