Special Issue "Theory and Application of Computational Intelligence and Deep Learning Paradigms in Robotics and Vision System"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Mechanical Engineering".

Deadline for manuscript submissions: closed (31 July 2019).

Special Issue Editor

Guest Editor
Prof. Dr. Dong Won Kim Website E-Mail
Department of Digital Electronics, Inha Technical College, 100 Inha-ro, Hagik 1(il)-dong, Nam-gu, Incheon, Korea
Interests: intelligent humanoid robot; autonomous multi-mobile robot systems; robot intelligence based on the deep learning and neuro-fuzzy system

Special Issue Information

Dear Colleagues,

Computational intelligence (CI) and deep learning—the study of adaptive mechanisms to enable or facilitate intelligent behavior in complex and changing environments—aims to exploit tolerance for imprecision, uncertainty and partial truth to achieve tractability, robustness and low solution costs. Therefore, these techniques are now being used widely by field engineers to solve a whole range of hitherto intractable problems. This Special Issue is intended to present and discuss theoretical and practical problems related to various robotic and control systems with computational intelligence approaches. In particular, this issue is devoted to new activities in mobile robot, humanoid robot, biologically-inspired robot, and vision systems. Original papers and survey papers are solicited for the Special Issue, covering research results as well as case studies and applications in related areas of interest.

Prof. Dr. Dong Won Kim
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 papers will be 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. 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 1500 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

  • Computational intelligence
  • Deep learning
  • Robotics
  • Vision system

Published Papers (4 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Open AccessArticle
Deep Learning-Based Classification of Weld Surface Defects
Appl. Sci. 2019, 9(16), 3312; https://doi.org/10.3390/app9163312 - 12 Aug 2019
Abstract
In order to realize the non-destructive intelligent identification of weld surface defects, an intelligent recognition method based on deep learning is proposed, which is mainly formed by convolutional neural network (CNN) and forest random. First, the high-level features are automatically learned through the [...] Read more.
In order to realize the non-destructive intelligent identification of weld surface defects, an intelligent recognition method based on deep learning is proposed, which is mainly formed by convolutional neural network (CNN) and forest random. First, the high-level features are automatically learned through the CNN. Random forest is trained with extracted high-level features to predict the classification results. Secondly, the weld surface defects images are collected and preprocessed by image enhancement and threshold segmentation. A database of weld surface defects is established using pre-processed images. Finally, comparative experiments are performed on the weld surface defects database. The results show that the accuracy of the method combined with CNN and random forest can reach 0.9875, and it also demonstrates the method is effective and practical. Full article
Show Figures

Figure 1

Open AccessArticle
A Novel Indirect Calibration Approach for Robot Positioning Error Compensation Based on Neural Network and Hand-Eye Vision
Appl. Sci. 2019, 9(9), 1940; https://doi.org/10.3390/app9091940 - 11 May 2019
Abstract
It is well known that most of the industrial robots have excellent repeatability in positioning. However, the absolute position errors of industrial robots are relatively poor, and in some cases the error may reach even several millimeters, which make it difficult to apply [...] Read more.
It is well known that most of the industrial robots have excellent repeatability in positioning. However, the absolute position errors of industrial robots are relatively poor, and in some cases the error may reach even several millimeters, which make it difficult to apply the robot system to vehicle assembly lines that need small position errors. In this paper, we have studied a method to reduce the absolute position error of robots using machine vision and neural network. The position/orientation of robot tool-end is compensated using a vision-based approach combined with a neural network, where a novel indirect calibration approach is presented in order to gather information for training the neural network. In the simulation, the proposed compensation algorithm was found to reduce the positional error to 98%. On average, the absolute position error was 0.029 mm. The application of the proposed algorithm in the actual robot experiment reduced the error to 50.3%, averaging 1.79 mm. Full article
Show Figures

Figure 1

Open AccessArticle
MIFT: A Moment-Based Local Feature Extraction Algorithm
Appl. Sci. 2019, 9(7), 1503; https://doi.org/10.3390/app9071503 - 11 Apr 2019
Abstract
We propose a local feature descriptor based on moment. Although conventional scale invariant feature transform (SIFT)-based algorithms generally use difference of Gaussian (DoG) for feature extraction, they remain sensitive to more complicated deformations. To solve this problem, we propose MIFT, an invariant feature [...] Read more.
We propose a local feature descriptor based on moment. Although conventional scale invariant feature transform (SIFT)-based algorithms generally use difference of Gaussian (DoG) for feature extraction, they remain sensitive to more complicated deformations. To solve this problem, we propose MIFT, an invariant feature transform algorithm based on the modified discrete Gaussian-Hermite moment (MDGHM). Taking advantage of MDGHM’s high performance to represent image information, MIFT uses an MDGHM-based pyramid for feature extraction, which can extract more distinctive extrema than the DoG, and MDGHM-based magnitude and orientation for feature description. We compared the proposed MIFT method performance with current best practice methods for six image deformation types, and confirmed that MIFT matching accuracy was superior of other SIFT-based methods. Full article
Show Figures

Figure 1

Open AccessArticle
Convolution Neural Network with Selective Multi-Stage Feature Fusion: Case Study on Vehicle Rear Detection
Appl. Sci. 2018, 8(12), 2468; https://doi.org/10.3390/app8122468 - 03 Dec 2018
Abstract
Vision-based vehicle detection is the most basic and important technology in advanced driver assistance systems. In this paper, we propose a vehicle detection framework using selective multi-stage features in convolutional neural networks (CNNs) to improve vehicle detection performance. A 10-layer CNN model was [...] Read more.
Vision-based vehicle detection is the most basic and important technology in advanced driver assistance systems. In this paper, we propose a vehicle detection framework using selective multi-stage features in convolutional neural networks (CNNs) to improve vehicle detection performance. A 10-layer CNN model was designed and visualization techniques were used to selectively extract features from the activation feature map, called selective multi-stage features. The proposed features contain characteristic vehicle image information and are more robust than traditional features against noise. We trained the AdaBoost algorithm using these features to implement a vehicle detector. The experimental results verified that the proposed vehicle detection framework exhibited better performance than previous frameworks. Full article
Show Figures

Figure 1

Back to TopTop