Special Issue "Machine Learning and AI Technology for Sustainability"

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Geography and Sustainability".

Deadline for manuscript submissions: 30 June 2021.

Special Issue Editors

Dr. Baihua Li
E-Mail Website
Guest Editor
Department of Computer Science, Loughborough University, Loughborough, LE11 3TU, UK
Interests: AI; machine learning; computer vision; deep learning; pattern recognition; human posture and activity recognition; autonomous vehicles; robot vision; human-computer interaction; medical image processing; signal and image processing; data modelling; computer graphics; virtual reality and visualization technology
Dr. Fei Chao
E-Mail Website1 Website2
Guest Editor
Department of Artificial Intelligence, Xiamen University, Xiamen, 361005, China; Ser II Cymru Fellow; Aberystwyth University; Ceredigion, SY23 3DB, UK
Interests: AI; machine learning; human-computer interaction; robotics; brain-robot interface; heuristic search; ensemble classfiers; deep reinforcement learning; planning and optimisation

Special Issue Information

Dear Colleagues,

Machine learning, artificial intelligence and a wide field of related technologies (in e.g. data science and intellgent systems) have contributed significantly to research into sustainability.  They have provided breakthrough concepts, state of the art technology and a wide range of innovations to tackle the problems we face.

The Guest Editors seek publications that address, but are not limited to, the following domains, related to the diverse aspects of machine learning and artificial intelligence for sustainability research:  

  • Machine Learning and AI for environment and health
  • Machine Learning and AI for agriculture and industry 4.0
  • Machine Learning and AI for air, water and climate sustainability
  • Machine Learning and AI for smart energy, renewable energy and green fuel
  • Machine Learning and AI for smart cities
  • Machine Learning and AI for sustainable policy making
  • Machine Learning and AI for traffic management and transportation
  • Machine learning benchmark datasets, platforms and tools for sustainability research

Dr. Baihua Li
Dr Fei Chao
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 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. Sustainability 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 1900 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 learning
  • Artificial intelligence
  • Sustainability
  • Deep learning
  • Intelligent systems
  • Industry 4.0
  • Robotics
  • Smart city
  • Cyberphysical systems
  • Edge computing
  • Data science
  • Cognitive computing
  • Big data

Published Papers (3 papers)

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Open AccessReview
Machine Learning in Predictive Maintenance towards Sustainable Smart Manufacturing in Industry 4.0
Sustainability 2020, 12(19), 8211; https://doi.org/10.3390/su12198211 - 05 Oct 2020
Cited by 11 | Viewed by 2767
Abstract
Recently, with the emergence of Industry 4.0 (I4.0), smart systems, machine learning (ML) within artificial intelligence (AI), predictive maintenance (PdM) approaches have been extensively applied in industries for handling the health status of industrial equipment. Due to digital transformation towards I4.0, information techniques, [...] Read more.
Recently, with the emergence of Industry 4.0 (I4.0), smart systems, machine learning (ML) within artificial intelligence (AI), predictive maintenance (PdM) approaches have been extensively applied in industries for handling the health status of industrial equipment. Due to digital transformation towards I4.0, information techniques, computerized control, and communication networks, it is possible to collect massive amounts of operational and processes conditions data generated form several pieces of equipment and harvest data for making an automated fault detection and diagnosis with the aim to minimize downtime and increase utilization rate of the components and increase their remaining useful lives. PdM is inevitable for sustainable smart manufacturing in I4.0. Machine learning (ML) techniques have emerged as a promising tool in PdM applications for smart manufacturing in I4.0, thus it has increased attraction of authors during recent years. This paper aims to provide a comprehensive review of the recent advancements of ML techniques widely applied to PdM for smart manufacturing in I4.0 by classifying the research according to the ML algorithms, ML category, machinery, and equipment used, device used in data acquisition, classification of data, size and type, and highlight the key contributions of the researchers, and thus offers guidelines and foundation for further research. Full article
(This article belongs to the Special Issue Machine Learning and AI Technology for Sustainability)
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Open AccessArticle
An LSTM Based Generative Adversarial Architecture for Robotic Calligraphy Learning System
Sustainability 2020, 12(21), 9092; https://doi.org/10.3390/su12219092 - 31 Oct 2020
Viewed by 546
Abstract
Robotic calligraphy is a very challenging task for the robotic manipulators, which can sustain industrial manufacturing. The active mechanism of writing robots require a large sized training set including sequence information of the writing trajectory. However, manual labelling work on those training data [...] Read more.
Robotic calligraphy is a very challenging task for the robotic manipulators, which can sustain industrial manufacturing. The active mechanism of writing robots require a large sized training set including sequence information of the writing trajectory. However, manual labelling work on those training data may cause the time wasting for researchers. This paper proposes a machine calligraphy learning system using a Long Short-Term Memory (LSTM) network and a generative adversarial network (GAN), which enables the robots to learn and generate the sequences of Chinese character stroke (i.e., writing trajectory). In order to reduce the size of the training set, a generative adversarial architecture combining an LSTM network and a discrimination network is established for a robotic manipulator to learn the Chinese calligraphy regarding its strokes. In particular, this learning system converts Chinese character stroke image into the trajectory sequences in the absence of the stroke trajectory writing sequence information. Due to its powerful learning ability in handling motion sequences, the LSTM network is used to explore the trajectory point writing sequences. Each generation process of the generative adversarial architecture contains a number of loops of LSTM. In each loop, the robot continues to write by following a new trajectory point, which is generated by LSTM according to the previously written strokes. The written stroke in an image format is taken as input to the next loop of the LSTM network until the complete stroke is finally written. Then, the final output of the LSTM network is evaluated by the discriminative network. In addition, a policy gradient algorithm based on reinforcement learning is employed to aid the robot to find the best policy. The experimental results show that the proposed learning system can effectively produce a variety of high-quality Chinese stroke writing. Full article
(This article belongs to the Special Issue Machine Learning and AI Technology for Sustainability)
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Open AccessArticle
Using Deep Learning and Machine Learning Methods to Diagnose Hailstorms in Large-Scale Thermodynamic Environments
Sustainability 2020, 12(24), 10499; https://doi.org/10.3390/su122410499 - 15 Dec 2020
Viewed by 443
Abstract
Hailstorms have caused damages in billions of dollars to industrial, electronic, and mechanical properties such as automobiles, buildings, roads, and aircrafts, as well as life threats to crop and cattle populations, due to their hazardous nature. Hence, the relevance of predicting hailstorms in [...] Read more.
Hailstorms have caused damages in billions of dollars to industrial, electronic, and mechanical properties such as automobiles, buildings, roads, and aircrafts, as well as life threats to crop and cattle populations, due to their hazardous nature. Hence, the relevance of predicting hailstorms in the future has significant scientific, economic, and societal benefits. However, climate models do not have adequate resolutions to explicitly resolve these subscale phenomena. One solution is to estimate the probability of these storms by using large-scale atmospheric thermodynamic environment variables from climate model outputs, but the existing methods only carried out experiments on small datasets limited to a region, country, or location and a large number of input features. Using one year of Tropical Rainfall Measuring Mission (TRMM) observations and European Center for Medium-Range Weather Forecasts (ECMWF) Re-Analysis Interim (ERA-Interim) reanalysis on a global scale, this paper develops two deep-learning-based models (an autoencoder and convolutional neural network (CNN)) as well as a machine learning approach (random forest) for hailstorm prediction by using only four attributes—convective potential energy, convective inhibition, 1–3 km wind shear, and warm cloud depth. In the experiments, the random forest approach produces the best hailstorm prediction performance compared to the other two methods. Full article
(This article belongs to the Special Issue Machine Learning and AI Technology for Sustainability)
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