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Applied Sciences
Special Issues
Machine Perception and Learning
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Machine Perception and Learning

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

Deadline for manuscript submissions: 20 May 2024 | Viewed by 2783

Special Issue Editor

Prof. Dr. Yi Ding

E-Mail Website
Guest Editor
School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
Interests: machine learning; image analysis

Special Issue Information

Dear Colleagues,

Machine perception and learning are highly interdisciplinary and draw on findings in psychology, neuroscience, machine learning, computer vision, and behavioral economics. The mission of this field is to enable machines to perceive and understand the real world in order for them to intelligently generate multimodal content and perform robustly in challenging tasks. Recently, researchers have started to apply a range of machine learning- and AI-based methods to a wide variety of data sources, including multispectral, medical imagery, camera images, live webcam streams and video data. The recurring objective is to design efficient and accurate algorithms for the automatic extraction of semantic information from the data source. There is clear scope for the further development of such approaches to enhance the performance of associated technologies, which is the key aim of this journal, such as machine learning, deep learning, and transfer learning methods and AI models.

We welcome original and well-grounded research papers on all aspects of the foundations of machine perception and learning. The contributions may be theoretical, methodological, algorithmic, empirical, integrative (connecting ideas and methods across machine perception and learning), or critical (e.g., principled analyses and arguments that draw attention to goals, assumptions, or approaches). The submissions should place emphasis on the demonstrated or potential impact of the research in addressing pressing societal challenges, e.g., health, food, environment, education, governance, among others. All submissions will be evaluated and scored for the significance and novelty of the contributions (research problems or questions addressed, methods, experiments, analyses), theoretical and/or empirical soundness of the claims, and clarity of exposition.

The topics of interest include, but are not limited to:

  • AI-related brain and cognitive science;
  • Machine perception and human–machine interaction;
  • Machine learning and data mining;
  • Multimodal emotion recognition;
  • Pattern recognition and computer vision;
  • Signal processing and recognition;
  • Medical image processing;
  • Semi-supervised and weakly supervised learning;
  • Intelligent information processing;
  • Natural language processing;
  • Network intelligence and mobile computing;
  • Intelligent control and decision;
  • Robotics and intelligent systems;
  • Auto-ML;
  • Information fusion from disparate sources.

Prof. Dr. Yi Ding
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.

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

  • AI-related brain and cognitive science
  • machine perception and human–machine interaction
  • machine learning and data mining
  • multimodal emotion recognition
  • pattern recognition and computer vision
  • signal processing and recognition
  • medical image processing
  • semi-supervised and weakly supervised learning
  • intelligent information processing
  • natural language processing
  • network intelligence and mobile computing
  • intelligent control and decision
  • robotics and intelligent systems
  • auto-ML
  • information fusion from disparate sources

Published Papers (3 papers)

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Research

21 pages, 3088 KiB  
Article
Passive TDOA Emitter Localization Using Fast Hyperbolic Hough Transform
by Gyula Simon and Ferenc Leitold
Appl. Sci. 2023, 13(24), 13301; https://doi.org/10.3390/app132413301 - 16 Dec 2023
Viewed by 625
Abstract
A fast Hough transform (HT)-based hyperbolic emitter localization system is proposed to process time difference of arrival (TDOA) measurements. The position-fixing problem is provided for cases where the source is known to be on a given plane (i.e., the elevation of the source [...] Read more.
A fast Hough transform (HT)-based hyperbolic emitter localization system is proposed to process time difference of arrival (TDOA) measurements. The position-fixing problem is provided for cases where the source is known to be on a given plane (i.e., the elevation of the source is known), while the sensors can be deployed anywhere in the three-dimensional space. The proposed solution provides fast evaluation and guarantees the determination of the global optimum. Another favorable property of the proposed solution is that it is robust against faulty sensor measurements (outliers). A fast evaluation method involving the hyperbolic Hough transform is proposed, and the global convergence property of the algorithm is proven. The performance of the algorithm is compared to that of the least-squares solution, other HT-based solutions, and the theoretical limit (the Cramér–Rao lower bound), using simulations and real measurement examples. Full article
(This article belongs to the Special Issue Machine Perception and Learning)
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17 pages, 12887 KiB  
Article
Deep Neural Network-Based Autonomous Voltage Control for Power Distribution Networks with DGs and EVs
by Durim Musiqi, Vjosë Kastrati, Alessandro Bosisio and Alberto Berizzi
Appl. Sci. 2023, 13(23), 12690; https://doi.org/10.3390/app132312690 - 27 Nov 2023
Viewed by 821
Abstract
This paper makes use of machine learning as a tool for voltage regulation in distribution networks that contain electric vehicles and a large production from distributed generation. The methods of voltage regulation considered in this study are electronic on-load tap changers and line [...] Read more.
This paper makes use of machine learning as a tool for voltage regulation in distribution networks that contain electric vehicles and a large production from distributed generation. The methods of voltage regulation considered in this study are electronic on-load tap changers and line voltage regulators. The analyzed study-case represents a real-life feeder which operates at 10 kV. It has 9 photovoltaic systems with various peak installed powers, 2 electric vehicle charging stations, and 41 secondary substations, each with an equivalent load. Measurement data of loads and irradiation data of photovoltaic systems were collected hourly for two years. Those data are used as inputs in the feeder’s model in DigSilent PowerFactory where Quasi-Dynamic simulations are run. That will provide the correct tap positions as outputs. These inputs and outputs will then serve to train a Deep Neural Network which later will be used to predict the correct tap positions on input data it has not seen before. Results show that ML in general and DNN specifically show usefulness and robustness in predicting correct tap positions with very small computational requirements. Full article
(This article belongs to the Special Issue Machine Perception and Learning)
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18 pages, 4434 KiB  
Article
Enhancing Anomaly Detection Models for Industrial Applications through SVM-Based False Positive Classification
by Ji Qiu, Hongmei Shi, Yuhen Hu and Zujun Yu
Appl. Sci. 2023, 13(23), 12655; https://doi.org/10.3390/app132312655 - 24 Nov 2023
Cited by 1 | Viewed by 1106
Abstract
Unsupervised anomaly detection models are crucial for the efficiency of industrial applications. However, frequent false alarms hinder the widespread adoption of unsupervised anomaly detection, especially in fault detection tasks. To this end, our research delves into the dependence of false alarms on the [...] Read more.
Unsupervised anomaly detection models are crucial for the efficiency of industrial applications. However, frequent false alarms hinder the widespread adoption of unsupervised anomaly detection, especially in fault detection tasks. To this end, our research delves into the dependence of false alarms on the baseline anomaly detector by analyzing the high-response regions in anomaly maps. We introduce an SVM-based false positive classifier as a post-processing module, which identifies false alarms from positive predictions at the object level. Moreover, we devise a sample synthesis strategy that generates synthetic false positives from the trained baseline detector while producing synthetic defect patch features from fuzzy domain knowledge. Following comprehensive evaluations, we showcase substantial performance enhancements in two advanced out-of-distribution anomaly detection models, Cflow and Fastflow, across image and pixel-level anomaly detection performance metrics. Substantive improvements are observed in two distinct industrial applications, with notable instances of elevating the image-level F1-score from 46.15% to 78.26% in optimal scenarios and boosting pixel-level AUROC from 72.36% to 94.74%. Full article
(This article belongs to the Special Issue Machine Perception and Learning)
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