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Intelligent Control for Robotics

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

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 2318

Special Issue Editor

School of Information Science and Technology, Dalian Maritime University, Dalian 116026, China
Interests: intelligent control; pattern recognition; human–computer interaction

Special Issue Information

Dear Colleagues,

Owing to its excellent capacity for intelligent interaction and control, robotics has been adopted in many industries, such as manufacturing, healthcare, transportation, and disaster relief. Researchers have been committed to the exploration of novel approaches, model architectures and products to improve the intelligence of robotics from various perspectives, including performance, reliability, robustness, etc.

The intelligent control of robotics can be divided into intelligent interaction and intelligent computing. Areas relevant to intelligent interaction include smart sensor design, human behavior learning, human–computer interaction, etc. Areas relevant to intelligent computing include deep learning, cognitive computing, route planning, etc. Therefore, this Special Issue intends to present new ideas, experimental results and products in the field.

We are soliciting papers on topics including, but not limited to:

  • New industrial robots;
  • Human behavior recognition and prediction;
  • Applications of smart sensors;
  • Human–computer interaction;
  • Control algorithms and models based on deep learning;
  • Fault diagnosis and identification;
  • Intelligent manufacturing;
  • Cognitive computing;
  • Route planning.

Dr. Yaqing Liu
Guest Editor

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.

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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

  • industrial robotics
  • deep learning
  • cognitive computing
  • intelligent manufacturing
  • human behavior recognition
  • smart sensors

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Published Papers (1 paper)

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Research

17 pages, 6931 KiB  
Article
A Fine-Tuning Based Approach for Daily Activity Recognition between Smart Homes
by Yunqian Yu, Kun Tang and Yaqing Liu
Appl. Sci. 2023, 13(9), 5706; https://doi.org/10.3390/app13095706 - 5 May 2023
Cited by 18 | Viewed by 2004
Abstract
Daily activity recognition between different smart home environments faces some challenges, such as an insufficient amount of data and differences in data distribution. However, a deep network requires a large amount of labeled data for training. Additionally, inconsistent data distribution can lead to [...] Read more.
Daily activity recognition between different smart home environments faces some challenges, such as an insufficient amount of data and differences in data distribution. However, a deep network requires a large amount of labeled data for training. Additionally, inconsistent data distribution can lead to over-fitting of network learning. Additionally, the time cost of training the network from scratch is too high. In order to solve the above problems, this paper proposes a fine-tuning method suitable for daily activity recognition, which is the first application of this method in our field. Firstly, we unify the sensor space and activity space to reduce the variability in heterogeneous environments. Then, the Word2Vec algorithm is used to transform the activity samples into digital vectors recognizable by the network. Finally, the deep network is fine-tuned to transfer knowledge and complete the recognition task. Additionally, we try to train the network on public datasets. The results show that the network trained on a small dataset also has good transferability. It effectively improves the recognition accuracy and reduces the time cost and heavy data annotation. Full article
(This article belongs to the Special Issue Intelligent Control for Robotics)
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