Application of Deep Learning in Intelligent Machines

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Machine Design and Theory".

Deadline for manuscript submissions: 30 April 2025 | Viewed by 5720

Special Issue Editors


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Guest Editor
School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798, Singapore
Interests: deep learning; intelligent driving; computer vision
Special Issues, Collections and Topics in MDPI journals
Associate Professor, School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798, Singapore
Interests: automated driving; human–machine systems; intelligent electric vehicles; human–robot collaboration; cyber–physical systems
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Associate Professor, Automotive Engineering Research Institute, Jiangsu University, Zhenjiang, China
Interests: driving behavior analysis; path planning; autonomous vehicle; cooperative adaptive cruise control; traffic flow theory; intelligent transportation systems
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Associate Professor, School of Computer Science and Technology, Hainan University, Haikou, China
Interests: artificial intelligence; human–computer interaction; bioinformatics

Special Issue Information

Dear Colleagues,

In recent years, the application of deep learning algorithms in the development of intelligent machines has achieved remarkable advancements. Deep learning, which is a subset of artificial intelligence (AI), has demonstrated its potential to revolutionize various domains. For example, some computer vision-based deep learning algorithms can automatically extract hierarchical features from images, enabling machines to recognize objects, detect anomalies, and classify complex scenes. The integration of deep learning algorithms into robotics and autonomous systems has led to a new era of intelligent machines. Autonomous vehicles, for instance, employ deep neural networks to process sensor data, recognize road signs, and make real-time decisions, thus enhancing road safety and efficiency. In manufacturing, robots equipped with deep learning capabilities can perform complex tasks with precision, adapt to changing environments, and learn by observing human demonstrations.

This Special Issue is focused on the study and discovery of the applications of deep learning algorithms in intelligent machines, which mainly include intelligent robotics, autonomous driving, intelligent fault diagnosis, intelligent assembly, intelligent manufacturing, process optimization, supply chain management, and safety. Deep learning algorithms have become the key components of intelligent machines. Their ability to learn from data, adapt to different tasks, and provide superior performance has enabled for the development of intelligent machines. As technology continues to advance, the integration of deep learning into intelligent machines promises to reshape industries and improve quality of life for individuals worldwide.

Dr. Lie Yang
Dr. Chen Lyu
Dr. Qingchao Liu
Dr. Zilong Zhang
Guest Editors

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Keywords

  • intelligent machines
  • deep learning
  • intelligent robotics
  • autonomous driving
  • intelligent fault diagnosis
  • intelligent assembly
  • intelligent manufacturing
  • process optimization
  • intelligent management of supply chain
  • safety monitoring and forecasting

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Published Papers (2 papers)

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Research

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17 pages, 4492 KiB  
Article
Vibration Analysis for Fault Diagnosis in Induction Motors Using One-Dimensional Dilated Convolutional Neural Networks
by Xiaopeng Liu, Jianfeng Hong, Kang Zhao, Bingxiang Sun, Weige Zhang and Jiuchun Jiang
Machines 2023, 11(12), 1061; https://doi.org/10.3390/machines11121061 - 29 Nov 2023
Viewed by 2959
Abstract
Motor faults not only damage the motor body but also affect the entire production system. When the motor runs in a steady state, the characteristic frequency of the fault current is close to the fundamental frequency, so it is difficult to effectively extract [...] Read more.
Motor faults not only damage the motor body but also affect the entire production system. When the motor runs in a steady state, the characteristic frequency of the fault current is close to the fundamental frequency, so it is difficult to effectively extract the fault current components, such as the broken rotor bar. In this paper, according to the characteristics of electromagnetic force and vibration, when the rotor eccentricity and the broken bar occur, the vibration signal is used to analyze and diagnose the fault. Firstly, the frequency, order, and amplitude characteristics of electromagnetic force under rotor eccentricity and broken bar fault are analyzed. Then, the fault vibration acceleration value collected by a one-dimensional dilated convolution pair is extracted, and the SeLU activation function and residual connection are introduced to solve the problem of gradient disappearance and network degradation, and the fault motor model is established by combining average ensemble learning and SoftMax multi-classifier. Finally, experiments of normal rotor eccentricity and broken bar faults are carried out on 4-pole asynchronous motors. The experimental results show that the accuracy of the proposed method for motor fault detection can reach 99%, which meets the requirements of fault motor detection and is helpful for further application. Full article
(This article belongs to the Special Issue Application of Deep Learning in Intelligent Machines)
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Review

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29 pages, 2692 KiB  
Review
Novel Directions for Neuromorphic Machine Intelligence Guided by Functional Connectivity: A Review
by Mindula Illeperuma, Rafael Pina, Varuna De Silva and Xiaolan Liu
Machines 2024, 12(8), 574; https://doi.org/10.3390/machines12080574 - 20 Aug 2024
Viewed by 1533
Abstract
As we move into the next stages of the technological revolution, artificial intelligence (AI) that is explainable and sustainable is becoming a key goal for researchers across multiple domains. Leveraging the concept of functional connectivity (FC) in the human brain, this paper provides [...] Read more.
As we move into the next stages of the technological revolution, artificial intelligence (AI) that is explainable and sustainable is becoming a key goal for researchers across multiple domains. Leveraging the concept of functional connectivity (FC) in the human brain, this paper provides novel research directions for neuromorphic machine intelligence (NMI) systems that are energy-efficient and human-compatible. This review serves as an accessible review for multidisciplinary researchers introducing a range of concepts inspired by neuroscience and analogous machine learning research. These include possibilities to facilitate network integration and segregation in artificial architectures, a novel learning representation framework inspired by two FC networks utilised in human learning, and we explore the functional connectivity underlying task prioritisation in humans and propose a framework for neuromorphic machines to improve their task-prioritisation and decision-making capabilities. Finally, we provide directions for key application domains such as autonomous driverless vehicles, swarm intelligence, and human augmentation, to name a few. Guided by how regional brain networks interact to facilitate cognition and behaviour such as the ones discussed in this review, we move toward a blueprint for creating NMI that mirrors these processes. Full article
(This article belongs to the Special Issue Application of Deep Learning in Intelligent Machines)
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