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Machine Learning for Intelligent Engineering Systems and Applications 2021-2022

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

Deadline for manuscript submissions: closed (20 April 2022) | Viewed by 7897

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Prof. Dr. Anastass Dulamis

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Photogrammetry and Computer Vision Lab., National Technical University of Athens, 15773 Athens, Greece
Interests: Machine Learning; Intelligent Engineering

Special Issue Information

Dear Colleagues,

The latest advances in machine learning have contributed to various developments in many areas of interest to the engineering community. Data-driven or domain-oriented engineering applications are significantly benefitting from the latest developments in machine learning theories and methods (including deep, reinforcement, transfer, and extreme learning), but may also promote the development of learning algorithms, optimization approaches, fusion techniques for multimodal data, novel hardware, and network architectures. The rapid developments in these fields have also stimulated new research on sensors and sensor networks.

The purpose of this Special Issue is to provide a forum for engineers, data scientists, researchers, and practitioners to present new academic research and industrial developments in machine learning for engineering applications. This Special Issue gathers original research papers in the field, covering new theories, algorithms, systems, and new implementations and applications incorporating state-of-the-art machine learning techniques. Emphasis will be placed on systems that incorporate new sensors and their configuration. Review articles and works on performance evaluation and benchmark datasets are also solicited.

Domains of application of interest to the Special Issue include:

Research on sensors for new critical engineering applications;
Sensor networks and drones to survey critical infrastructure;
Software and hardware architectures for new sensorial systems managing critical infrastructure;
Electrical and mechanical engineering, production management and optimization, manufacturing, failure detection, energy management, and smart grids;
Robotics and automation, computer vision and pattern recognition applications, critical infrastructure protection;
Civil engineering, construction management and optimization, structural health monitoring, earthquake engineering, urban planning;
Transportation, hydraulics, water power, and environmental engineering;
Surveying and geospatial engineering, spatial planning, and remote sensing;
Materials science and engineering;
Biomedical engineering.

Prof. Dr. Anastass Dulamis
Guest Editor

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Published Papers (4 papers)

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Research

18 pages, 1140 KiB

Open AccessArticle

Capped Linex Metric Twin Support Vector Machine for Robust Classification

by Yifan Wang, Guolin Yu and Jun Ma

Sensors 2022, 22(17), 6583; https://0-doi-org.brum.beds.ac.uk/10.3390/s22176583 - 31 Aug 2022

Cited by 2 | Viewed by 1116

Abstract

In this paper, a novel robust loss function is designed, namely, capped linear loss function

L_{a ε}

. Simultaneously, we give some ideal and important properties of

L_{a ε}

, such as boundedness, nonconvexity and robustness. Furthermore, a new binary classification [...] Read more.

In this paper, a novel robust loss function is designed, namely, capped linear loss function

L_{a ε}

. Simultaneously, we give some ideal and important properties of

L_{a ε}

, such as boundedness, nonconvexity and robustness. Furthermore, a new binary classification learning method is proposed via introducing

L_{a ε}

, which is called the robust twin support vector machine (Linex-TSVM). Linex-TSVM can not only reduce the influence of outliers on Linex-SVM, but also improve the classification performance and robustness of Linex-SVM. Moreover, the effect of outliers on the model can be greatly reduced by introducing two regularization terms to realize the structural risk minimization principle. Finally, a simple and efficient iterative algorithm is designed to solve the non-convex optimization problem Linex-TSVM, and the time complexity of the algorithm is analyzed, which proves that the model satisfies the Bayes rule. Experimental results on multiple datasets demonstrate that the proposed Linex-TSVM can compete with the existing methods in terms of robustness and feasibility. Full article

(This article belongs to the Special Issue Machine Learning for Intelligent Engineering Systems and Applications 2021-2022)

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34 pages, 2726 KiB

Open AccessArticle

Multi-Swarm Algorithm for Extreme Learning Machine Optimization

by Nebojsa Bacanin, Catalin Stoean, Miodrag Zivkovic, Dijana Jovanovic, Milos Antonijevic and Djordje Mladenovic

Sensors 2022, 22(11), 4204; https://0-doi-org.brum.beds.ac.uk/10.3390/s22114204 - 31 May 2022

Cited by 21 | Viewed by 2145

Abstract

There are many machine learning approaches available and commonly used today, however, the extreme learning machine is appraised as one of the fastest and, additionally, relatively efficient models. Its main benefit is that it is very fast, which makes it suitable for integration within products that require models taking rapid decisions. Nevertheless, despite their large potential, they have not yet been exploited enough, according to the recent literature. Extreme learning machines still face several challenges that need to be addressed. The most significant downside is that the performance of the model heavily depends on the allocated weights and biases within the hidden layer. Finding its appropriate values for practical tasks represents an NP-hard continuous optimization challenge. Research proposed in this study focuses on determining optimal or near optimal weights and biases in the hidden layer for specific tasks. To address this task, a multi-swarm hybrid optimization approach has been proposed, based on three swarm intelligence meta-heuristics, namely the artificial bee colony, the firefly algorithm and the sine–cosine algorithm. The proposed method has been thoroughly validated on seven well-known classification benchmark datasets, and obtained results are compared to other already existing similar cutting-edge approaches from the recent literature. The simulation results point out that the suggested multi-swarm technique is capable to obtain better generalization performance than the rest of the approaches included in the comparative analysis in terms of accuracy, precision, recall, and f1-score indicators. Moreover, to prove that combining two algorithms is not as effective as joining three approaches, additional hybrids generated by pairing, each, two methods employed in the proposed multi-swarm approach, were also implemented and validated against four challenging datasets. The findings from these experiments also prove superior performance of the proposed multi-swarm algorithm. Sample code from devised ELM tuning framework is available on the GitHub. Full article

(This article belongs to the Special Issue Machine Learning for Intelligent Engineering Systems and Applications 2021-2022)

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19 pages, 2226 KiB

Open AccessArticle

A Novel Binary Hybrid PSO-EO Algorithm for Cryptanalysis of Internal State of RC4 Cipher

by Rizk M. Rizk-Allah, Hatem Abdulkader, Samah S. Abd Elatif, Wail S. Elkilani, Eslam Al Maghayreh, Habib Dhahri and Awais Mahmood

Sensors 2022, 22(10), 3844; https://0-doi-org.brum.beds.ac.uk/10.3390/s22103844 - 19 May 2022

Cited by 5 | Viewed by 1367

Abstract

Cryptography protects privacy and confidentiality. So, it is necessary to guarantee that the ciphers used are secure and cryptanalysis-resistant. In this paper, a new state recovery attack against the RC4 stream cipher is revealed. A plaintext attack is used in which the attacker has both the plaintext and the ciphertext, so they can calculate the keystream and reveal the cipher’s internal state. To increase the quality of answers to practical and recent real-world global optimization difficulties, researchers are increasingly combining two or more variations. PSO and EO are combined in a hybrid PSOEO in an uncertain environment. We may also convert this method to its binary form to cryptanalyze the internal state of the RC4 cipher. When solving the cryptanalysis issue with HBPSOEO, we discover that it is more accurate and quicker than utilizing both PSO and EO independently. Experiments reveal that our proposed fitness function, in combination with HBPSOEO, requires checking 10⁴ possible internal states; however, brute force attacks require checking 2¹²⁸ states. Full article

(This article belongs to the Special Issue Machine Learning for Intelligent Engineering Systems and Applications 2021-2022)

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23 pages, 14285 KiB

Open AccessArticle

Multi-End Physics-Informed Deep Learning for Seismic Response Estimation

by Peng Ni, Limin Sun, Jipeng Yang and Yixian Li

Sensors 2022, 22(10), 3697; https://0-doi-org.brum.beds.ac.uk/10.3390/s22103697 - 12 May 2022

Cited by 6 | Viewed by 2541

Abstract

As a structural health monitoring (SHM) system can hardly measure all the needed responses, estimating the target response from the measured responses has become an important task. Deep neural networks (NNs) have a strong nonlinear mapping ability, and they are widely used in response reconstruction works. The mapping relation among different responses is learned by a NN given a large training set. In some cases, however, especially for rare events such as earthquakes, it is difficult to obtain a large training dataset. This paper used a convolution NN to reconstruct structure response under rare events with small datasets, and the main innovations include two aspects. Firstly, we proposed a multi-end autoencoder architecture with skip connections, which compresses the parameter space, to estimate the unmeasured responses. It extracts the shared patterns in the encoder and reconstructs different types of target responses in varied branches of the decoder. Secondly, the physics-based loss function, derived from the dynamic equilibrium equation, was adopted to guide the training direction and suppress the overfitting effect. The proposed NN takes the acceleration at limited positions as input. The output is the displacement, velocity, and acceleration responses at all positions. Two numerical studies validated that the proposed framework applies to both linear and nonlinear systems. The physics-informed NN had a higher performance than the ordinary NN with small datasets, especially when the training data contained noise. Full article

(This article belongs to the Special Issue Machine Learning for Intelligent Engineering Systems and Applications 2021-2022)

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