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Human-Computer Interaction for Industrial Applications
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Human-Computer Interaction for Industrial Applications

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

Deadline for manuscript submissions: closed (10 April 2022) | Viewed by 19642

Special Issue Editors

Dr. Laura Garcia-Hernandez

E-Mail Website
Guest Editor
Department of Rural Engineering, University of Córdoba, Av. de Medina Azahara, 5, 14071 Córdoba, Spain
Interests: UA-FLP; evolutionary algorithms; engineering education; project management
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Lorenzo Salas-Morera

E-Mail Website
Guest Editor
Area of Project Engineering, University of Cordoba, 14071 Córdoba, Spain
Interests: UA-FLP; plant layout design; evolutionary algorithms; interactive algorithms; project management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Automation, digitalization, and robotics have provided an opportunity for integrating human behavior into computational intelligence applications, such as neural networks, fuzzy systems, and evolutionary computation for many industrial fields. On the one hand, perceptual integration from cognitive sciences is being considered in the design of human–computer interaction (HCI) systems to decrease users’ visual fatigue; on the other hand, artificial intelligence provides the opportunity to adapt systems to the characteristics of users, improving the way in which they interact with them.

This Special Issue aims to publish high-quality papers about novel tendencies of integrating human interaction in various industrial applications. The recommended topics include, but are not limited to:

  • The industrial applications of computational intelligence in human-related datasets;
  • The integration of human cognitive processes;
  • Human–computer interaction for industrial applications;
  • Eye/hand/face/body tracking and activity recognition;
  • Natural language processing.

Prof. Dr. Laura Garcia-Hernandez
Prof. Dr. Lorenzo Salas-Morera
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. 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

  • human–computer interaction
  • industry
  • artificial intelligence
  • interactive systems
  • industrial applications
  • computer-aided design
  • users

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Published Papers (5 papers)

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Research

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20 pages, 2475 KiB  
Article
Prevention of Falls from Heights in Construction Using an IoT System Based on Fuzzy Markup Language and JFML
by María del Carmen Rey-Merchán, Antonio López-Arquillos and José Manuel Soto-Hidalgo
Appl. Sci. 2022, 12(12), 6057; https://doi.org/10.3390/app12126057 - 14 Jun 2022
Cited by 11 | Viewed by 3400
Abstract
The main cause of fatal accidents in the construction sector are falls from height (FFH) and the inappropriate use of a harness is commonly associated with these fatalities. Traditional methods, such as onsite inspections, safety communication, or safety training, are not enough to [...] Read more.
The main cause of fatal accidents in the construction sector are falls from height (FFH) and the inappropriate use of a harness is commonly associated with these fatalities. Traditional methods, such as onsite inspections, safety communication, or safety training, are not enough to mitigate accidents caused by FFH associated with a poor management in the use of a harness. Although some technological solutions for the automated monitoring of workers could improve safety conditions, their use is not frequent due to the particularities of construction sites: complexity, dynamic environments, outdoor workplaces, etc. Then, the integration of expert knowledge with technology is a key issue. Fuzzy logic systems (FLS) and Internet of Things (IoT) present many potential benefits, such as real-time decisions being made based on FLS and data from sensors. In the current research, the development and test of an IoT system integrated with the Java Fuzzy Markup Language Library for FLS, to support experts’ decision making in FFH, is proposed. The proposal was checked in four construction scenarios based on working conditions with different levels of risk of FFH and obtained promising results. Full article
(This article belongs to the Special Issue Human-Computer Interaction for Industrial Applications)
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17 pages, 1864 KiB  
Article
A Novel Artificial Neural Network to Predict Compressive Strength of Recycled Aggregate Concrete
by David Suescum-Morales, Lorenzo Salas-Morera, José Ramón Jiménez and Laura García-Hernández
Appl. Sci. 2021, 11(22), 11077; https://doi.org/10.3390/app112211077 - 22 Nov 2021
Cited by 17 | Viewed by 3036
Abstract
Most regulations only allow the use of the coarse fraction of recycled concrete aggregate (RCA) for the manufacture of new concrete, although the heterogeneity of RCA makes it difficult to predict the compressive strength of concrete, which is an obstacle to the incorporation [...] Read more.
Most regulations only allow the use of the coarse fraction of recycled concrete aggregate (RCA) for the manufacture of new concrete, although the heterogeneity of RCA makes it difficult to predict the compressive strength of concrete, which is an obstacle to the incorporation of RCA in concrete production. The compressive strength of recycled aggregate concrete is closely related to the dosage of its constituents. This article proposes a novel artificial neural network (ANN) model to predict the 28-day compressive strength of recycled aggregate concrete. The ANN used in this work has 11 neurons in the input layer: the mass of cement, fly ash, water, superplasticizer, fine natural aggregate, coarse natural or recycled aggregate, and their properties, such as: sand fineness modulus of sand, water absorption capacity, saturated surface dry density of the coarse aggregate mix and the maximum particle size. Two training methods were used for the ANN combining 15 and 20 hidden layers: Levenberg–Marquardt (LM) and Bayesian Regularization (BR). A database with 177 mixes selected from 15 studies incorporating RCA were selected, with the aim of having an underlying set of data heterogeneous enough to demonstrate the efficiency of the proposed approach, even when data are heterogeneous and noisy, which is the main finding of this work. Full article
(This article belongs to the Special Issue Human-Computer Interaction for Industrial Applications)
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28 pages, 1771 KiB  
Article
A Multi-User Interactive Coral Reef Optimization Algorithm for Considering Expert Knowledge in the Unequal Area Facility Layout Problem
by Lorenzo Salas-Morera, Laura García-Hernández and Carlos Carmona-Muñoz
Appl. Sci. 2021, 11(15), 6676; https://doi.org/10.3390/app11156676 - 21 Jul 2021
Cited by 3 | Viewed by 2279
Abstract
The problem of Unequal Area Facility Layout Planning (UA-FLP) has been addressed by a large number of approaches considering a set of quantitative criteria. Moreover, more recently, the personal qualitative preferences of an expert designer or decision-maker (DM) have been taken into account [...] Read more.
The problem of Unequal Area Facility Layout Planning (UA-FLP) has been addressed by a large number of approaches considering a set of quantitative criteria. Moreover, more recently, the personal qualitative preferences of an expert designer or decision-maker (DM) have been taken into account too. This article deals with capturing more than a single DM’s personal preferences to obtain a common and collaborative design including the whole set of preferences from all the DMs to obtain more complex, complete, and realistic solutions. To the best of our knowledge, this is the first time that the preferences of more than one expert designer have been considered in the UA-FLP. The new strategy has been implemented on a Coral Reef Optimization (CRO) algorithm using two techniques to acquire the DMs’ evaluations. The first one demands the simultaneous presence of all the DMs, while the second one does not. Both techniques have been tested over three well-known problem instances taken from the literature and the results show that it is possible to obtain sufficient designs capturing all the DMs’ personal preferences and maintaining low values of the quantitative fitness function. Full article
(This article belongs to the Special Issue Human-Computer Interaction for Industrial Applications)
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14 pages, 19881 KiB  
Article
An Enhanced Deep Convolutional Neural Network for Classifying Indian Classical Dance Forms
by Nikita Jain, Vibhuti Bansal, Deepali Virmani, Vedika Gupta, Lorenzo Salas-Morera and Laura Garcia-Hernandez
Appl. Sci. 2021, 11(14), 6253; https://doi.org/10.3390/app11146253 - 6 Jul 2021
Cited by 28 | Viewed by 4803
Abstract
Indian classical dance (ICD) classification is an interesting subject because of its complex body posture. It provides a stage to experiment with various computer vision and deep learning concepts. With a change in learning styles, automated teaching solutions have become inevitable in every [...] Read more.
Indian classical dance (ICD) classification is an interesting subject because of its complex body posture. It provides a stage to experiment with various computer vision and deep learning concepts. With a change in learning styles, automated teaching solutions have become inevitable in every field, from traditional to online platforms. Additionally, ICD forms an essential part of a rich cultural and intangible heritage, which at all costs must be modernized and preserved. In this paper, we have attempted an exhaustive classification of dance forms into eight categories. For classification, we have proposed a deep convolutional neural network (DCNN) model using ResNet50, which outperforms various state-of-the-art approaches. Additionally, to our surprise, the proposed model also surpassed a few recently published works in terms of performance evaluation. The input to the proposed network is initially pre-processed using image thresholding and sampling. Next, a truncated DCNN based on ResNet50 is applied to the pre-processed samples. The proposed model gives an accuracy score of 0.911. Full article
(This article belongs to the Special Issue Human-Computer Interaction for Industrial Applications)
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Review

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27 pages, 2708 KiB  
Review
Multimodal Natural Human–Computer Interfaces for Computer-Aided Design: A Review Paper
by Hongwei Niu, Cees Van Leeuwen, Jia Hao, Guoxin Wang and Thomas Lachmann
Appl. Sci. 2022, 12(13), 6510; https://doi.org/10.3390/app12136510 - 27 Jun 2022
Cited by 12 | Viewed by 4564
Abstract
Computer-aided design (CAD) systems have advanced to become a critical tool in product design. Nevertheless, they still primarily rely on the traditional mouse and keyboard interface. This limits the naturalness and intuitiveness of the 3D modeling process. Recently, a multimodal human–computer interface (HCI) [...] Read more.
Computer-aided design (CAD) systems have advanced to become a critical tool in product design. Nevertheless, they still primarily rely on the traditional mouse and keyboard interface. This limits the naturalness and intuitiveness of the 3D modeling process. Recently, a multimodal human–computer interface (HCI) has been proposed as the next-generation interaction paradigm. Widening the use of a multimodal HCI provides new opportunities for realizing natural interactions in 3D modeling. In this study, we conducted a literature review of a multimodal HCI for CAD to summarize the state-of-the-art research and establish a solid foundation for future research. We explore and categorize the requirements for natural HCIs and discuss paradigms for their implementation in CAD. Following this, factors to evaluate the system performance and user experience of a natural HCI are summarized and analyzed. We conclude by discussing challenges and key research directions for a natural HCI in product design to inspire future studies. Full article
(This article belongs to the Special Issue Human-Computer Interaction for Industrial Applications)
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