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Sensing and Machine Learning Control: Progress and Applications
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Sensing and Machine Learning Control: Progress and Applications

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 5982

Special Issue Editor

Dr. Félix Hernández del Olmo

E-Mail Website
Guest Editor
Department of Artificial Intelligence, National Distance Education University (UNED), 28040 Madrid, Spain
Interests: machine learning; reinforcement learning; adaptive control; recommender systems; user modeling;wastewater systems; adaptive predictive control; adaptive interfaces
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Special Issue "Sensing and Machine Learning Control: Progress and Applications" delves into the dynamic integration of sensing technology and machine learning (ML) with control systems, highlighting cutting-edge research and innovative applications. It brings together a collection of studies that demonstrate how ML techniques, enhanced by advanced sensing technologies, can significantly improve control processes across various domains.

The issue covers diverse topics, including reinforcement learning for developing adaptive control strategies, the application of deep learning for predictive maintenance and fault detection, and the design of hybrid systems combining traditional control theories with advanced ML algorithms. These contributions illustrate how ML, coupled with precise and real-time sensor data, can address complex, nonlinear dynamics and improve the efficiency, accuracy, and robustness of control systems.

Practical applications featured in the issue span multiple industries, such as autonomous vehicles, robotics, energy management, and aerospace. Case studies showcase the practical benefits of sensor-enhanced, ML-driven control, such as improved performance, cost savings, and increased safety.

This Special Issue serves as a crucial resource for researchers, engineers, and practitioners, highlighting ongoing advancements and encouraging further exploration into the synergy between sensing technology, machine learning, and control systems. It underscores the significant potential of integrating sensors with ML to revolutionize traditional control methodologies and drive future innovations.

Dr. Félix Hernández del Olmo
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. Sensors 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 2600 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
  • sensing technology
  • autonomous vehicles
  • robotics
  • energy management
  • ML-driven control

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Published Papers (3 papers)

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40 pages, 2488 KiB  
Article
Analysis of Autonomous Penetration Testing Through Reinforcement Learning and Recommender Systems
by Ariadna Claudia Moreno, Aldo Hernandez-Suarez, Gabriel Sanchez-Perez, Linda Karina Toscano-Medina, Hector Perez-Meana, Jose Portillo-Portillo, Jesus Olivares-Mercado and Luis Javier García Villalba
Sensors 2025, 25(1), 211; https://doi.org/10.3390/s25010211 - 2 Jan 2025
Viewed by 2186
Abstract
Conducting penetration testing (pentesting) in cybersecurity is a crucial turning point for identifying vulnerabilities within the framework of Information Technology (IT), where real malicious offensive behavior is simulated to identify potential weaknesses and strengthen preventive controls. Given the complexity of the tests, time [...] Read more.
Conducting penetration testing (pentesting) in cybersecurity is a crucial turning point for identifying vulnerabilities within the framework of Information Technology (IT), where real malicious offensive behavior is simulated to identify potential weaknesses and strengthen preventive controls. Given the complexity of the tests, time constraints, and the specialized level of expertise required for pentesting, analysis and exploitation tools are commonly used. Although useful, these tools often introduce uncertainty in findings, resulting in high rates of false positives. To enhance the effectiveness of these tests, Machine Learning (ML) has been integrated, showing significant potential for identifying anomalies across various security areas through detailed detection of underlying malicious patterns. However, pentesting environments are unpredictable and intricate, requiring analysts to make extensive efforts to understand, explore, and exploit them. This study considers these challenges, proposing a recommendation system based on a context-rich, vocabulary-aware transformer capable of processing questions related to the target environment and offering responses based on necessary pentest batteries evaluated by a Reinforcement Learning (RL) estimator. This RL component assesses optimal attack strategies based on previously learned data and dynamically explores additional attack vectors. The system achieved an F1 score and an Exact Match rate over 97.0%, demonstrating its accuracy and effectiveness in selecting relevant pentesting strategies. Full article
(This article belongs to the Special Issue Sensing and Machine Learning Control: Progress and Applications)
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20 pages, 2170 KiB  
Article
Enhanced Conditional GAN for High-Quality Synthetic Tabular Data Generation in Mobile-Based Cardiovascular Healthcare
by Malak Alqulaity and Po Yang
Sensors 2024, 24(23), 7673; https://doi.org/10.3390/s24237673 - 30 Nov 2024
Viewed by 1453
Abstract
The generation of synthetic tabular data has emerged as a critical task in various fields, particularly in healthcare, where data privacy concerns limit the availability of real datasets for research and analysis. This paper presents an enhanced Conditional Generative Adversarial Network (GAN) architecture [...] Read more.
The generation of synthetic tabular data has emerged as a critical task in various fields, particularly in healthcare, where data privacy concerns limit the availability of real datasets for research and analysis. This paper presents an enhanced Conditional Generative Adversarial Network (GAN) architecture designed for generating high-quality synthetic tabular data, with a focus on cardiovascular disease datasets that encompass mixed data types and complex feature relationships. The proposed architecture employs specialized sub-networks to process continuous and categorical variables separately, leveraging metadata such as Gaussian Mixture Model (GMM) parameters for continuous attributes and embedding layers for categorical features. By integrating these specialized pathways, the generator produces synthetic samples that closely mimic the statistical properties of the real data. Comprehensive experiments were conducted to compare the proposed architecture with two established models: Conditional Tabular GAN (CTGAN) and Tabular Variational AutoEncoder (TVAE). The evaluation utilized metrics such as the Kolmogorov–Smirnov (KS) test for continuous variables, the Jaccard coefficient for categorical variables, and pairwise correlation analyses. Results indicate that the proposed approach attains a mean KS statistic of 0.3900, demonstrating strong overall performance that outperforms CTGAN (0.4803) and is comparable to TVAE (0.3858). Notably, our approach shows lowest KS statistics for key continuous features, such as total cholesterol (KS = 0.0779), weight (KS = 0.0861), and diastolic blood pressure (KS = 0.0957), indicating its effectiveness in closely replicating real data distributions. Additionally, it achieved a Jaccard coefficient of 1.00 for eight out of eleven categorical variables, effectively preserving categorical distributions. These findings indicate that the proposed architecture captures both distributions and dependencies, providing a robust solution in supporting mobile personalized cardiovascular disease prevention systems. Full article
(This article belongs to the Special Issue Sensing and Machine Learning Control: Progress and Applications)
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14 pages, 5838 KiB  
Article
Condition Monitoring Using a Latent Space of Variational Autoencoder Trained Only on a Healthy Machine
by Iwona Komorska and Andrzej Puchalski
Sensors 2024, 24(21), 6825; https://doi.org/10.3390/s24216825 - 24 Oct 2024
Viewed by 1580
Abstract
Machine learning generative models have opened up a new perspective for automated machine diagnostics. These methods improve decision-making by extracting features, classifying, and creating new observations using deep neural networks. Generative modeling aims to determine the joint distribution of input data. This contrasts [...] Read more.
Machine learning generative models have opened up a new perspective for automated machine diagnostics. These methods improve decision-making by extracting features, classifying, and creating new observations using deep neural networks. Generative modeling aims to determine the joint distribution of input data. This contrasts traditional methods used in diagnostics based on discriminative models and the conditional probability distribution of the target variable at known feature values. In the variational autoencoder (VAE) algorithms trained by the authors, the parameters of diagnostic features are random variables, the distributions of which can be approximated based on data, and the identification of probability distributions is based on variational inference. Variational inference is a tool that deals with difficult statistical problems and is usually faster than classical methods. VAEs can detect anomalies, predict failures, and optimize processes. This paper proposes an unsupervised approach to fault diagnosis using only healthy data with automatic feature extraction from the continuous probabilistic latent subspace of the VAE encoder and reduction in PCA or t-SNE. The solution, verified in the example of simulation data, is a response to a common problem related to the lack or difficulty of obtaining marked data in defected states of devices and mechanical structures. Full article
(This article belongs to the Special Issue Sensing and Machine Learning Control: Progress and Applications)
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