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| CONTENTS |
Virtual Special Issue
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Abstract
The primary structural element used by fingerprint recognition systems is the ridge orientation structure which provides a geometric prior for enhancing images, extracting features and comparing them to one another. However, most of the previous adversarial studies have operated in the pixel domain and therefore, structural manipulation within the orientation manifold has been left mostly unexplored. Therefore, we introduce a ridge orientation perturbation framework that is constrained based on ridge flow characteristics such that the generated perturbations preserve the smoothness and singularity properties of the original ridge flows. In doing so, we ensure that our attacks are biometrically plausible but induce instability to the verification process. We tested the proposed attack on controlled (FVC2004) and forensic latent (NIST SD27) databases using both classical minutiae-based and CNNbased matchers. Our experimental results show that severe degradation can be achieved when structurally consistent perturbations are applied, where the equal error rate (EER) increased on the NIST SD27 database. Our findings indicate that ridge orientations represent a critical structural component of fingerprint systems that is shared across multiple recognition paradigms and illustrate a previously under-characterized geometric vulnerability.
Key Words
Adversarial biometrics; Attack Success Rate (ASR); biometric security; Equal Error Rate (EER); fingerprint recognition; minutiae-based matching; ridge orientation perturbation; ROC analysis
Address
(1) Enshirah Altarawneh:
Department of Computer Engineering, Faculty of Engineering, The Hashemite University, Zarqa, Jordan;
(2) Jawdat S. Alkasassbeh, Khalaf Y. Alzyoud:
Department of Electrical Engineering, Faculty of Engineering Technology, Al-Balqa Applied University, Amman, Jordan;
(3) Sattam Almatarneh:
Faculty of Information Technology, Zarqa University, Zarqa, Jordan;
(4) Redhwan Algabri:
Department of Computer Science and Engineering, Sejong University, Seoul, 05006, Republic of Korea.
Abstract
This study presents a comparative development of single- and multi-output Artificial Neural Network (ANN) surrogate models for accurately predicting the first five natural frequencies of a rotating Timoshenko beam. Specifically, a comprehensive dataset consisting of 17,576 samples was generated using a high-fidelity Chebyshev spectral collocation method over a wide range of dimensionless design parameters. An initial correlation analysis has been conducted to effectively investigate the statistical relationships between the design parameters and the associated first five natural frequencies. Based on the initial screening, two ANN modeling strategies have been established: a unified multi-output ANN model capable of simultaneously predicting all five natural frequencies, and a set of five independent single-output ANN models, each optimized for a specific natural frequency. The models have been trained, validated, and evaluated using a 70-15-15 data split and compared against standard performance metrics from the literature, including the Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Coefficient of Determination (R2), along with the computational efficiency required for the model development. The results demonstrate that both ANN approaches significantly outperform a baseline linear regression model, confirming the strongly nonlinear nature of the problem under study. The single-output ANN models consistently achieved very high prediction accuracy across all modes, with R
Key Words
artificial neural networks; chebyshev spectral collocation; data-driven prediction; natural frequencies; rotating Timoshenko beam
Address
(1) Sameer Al-Dahidi, Ma'en S. Sari:
Department of Mechanical and Maintenance Engineering, School of Applied Technical Sciences,
German Jordanian University, Amman 11180, Jordan;
(2) Mohammad Alrbai:
Department of Mechanical Engineering, School of Engineering, University of Jordan, Amman 11942, Jordan.
Abstract
Energy harvesters are a promising solution for powering low-frequency, low-power autonomous devices. However, their nonlinear electromechanical behavior makes accurate modeling, performance prediction, and rapid design optimization challenging tasks, especially using conventional analytical and numerical techniques. These techniques are often constrained by simplifying assumptions and high computational cost. This study proposes a Deep Neural Network (DNN)–based and a Feedforward Neural Network (FNN)-based modeling framework for fast and accurate prediction of the dynamic response and harvested power of electret-based microcantilever energy harvesters under a wide range of operating conditions without reliance on simplifying assumptions. A comprehensive dataset comprising 27 high-fidelity numerical simulations of a single-degree-of-freedom model is employed for a wide range of operating conditions, including variations in excitation frequency, electret surface voltage, base acceleration, and electrical resistance load. The proposed DNN and FNN models are benchmarked against a numerical integration technique. Results demonstrate that the DNN accurately captures key nonlinear phenomena, including resonance shifts, bandwidth variation, nonlinear softening effects, and pull-in instability, while providing near-instantaneous predictions of performance once trained. Quantitatively, the DNN achieves a total Root Mean Square Error (RMSE) and a total Mean Absolute Error (MAE) of 1.10 × 10−5 and 3.73 × 10−6, respectively, on training data, outperforming the FNN by more than one order of magnitude. Furthermore, the DNN exhibits superior generalization to unseen operating conditions and maintains robust predictive capability in nonlinear regimes. By eliminating the need for simplifying assumptions and computationally intensive simulations, the proposed approach offers a powerful and efficient alternative for rapid design optimization, real-time implementation, and adaptive control of electret-based microcantilever energy harvesting systems. This work addresses a critical gap in the literature and underscores the potential of deep learning for advancing intelligent, self-powered microsystems.
Key Words
Artificial Intelligence; Deep Neural Network; electret; energy harvesters; Feedforward Neural Network; microcantilever; numerical integration
Address
(1) Bashar Hammad:
Department of Mechanical and Maintenance Engineering, German Jordanian University, Amman, 11182, Jordan;
(2) Ahmad Jobran Al-Mahasneh:
Department of Mechatronics Engineering, Philadelphia University, Amman, 19392, Jordan;
(3) Mohammed Abu Mallouh:
Department of Mechatronics Engineering, Faculty of Engineering, The Hashemite University, Zarqa, Jordan;
(4) Basel Jouda:
Department of Electrical Engineering, University of Prince Mugrin, Madinah, 42241, Saudi Arabia.
Abstract
Sustainable concrete mix design requires balancing mechanical performance with environmental and resource objectives, a multi-objective problem traditionally addressed through heuristic or trial-and-error methods. This study presents a surrogate-assisted multi-objective genetic optimization framework to model sustainable concrete design as a multi-player game, where compressive strength, CO2 emissions, energy consumption, and material usage are treated as rational players. Random Forest regressors serve as surrogate models for each objective, enabling efficient exploration of the design space. Two optimization approaches are compared: a weighted-fitness genetic algorithm (GA) and a NSGA-II-based multi-objective NSGA-II knee-point optimization with knee-point detection. The GA produces a compromise mix emphasizing strength, but at the cost of environmental and resource objectives, achieving 141.3 kg/cm2 strength, 433.1 kg CO2, and 2373.7 kWh energy. In contrast, the NSGA identifies a Pareto-optimal knee-point mix yielding 478.7 kg/cm2 strength, 247.5 kg CO2, and 1262.2 kWh energy, demonstrating substantial improvements across all objectives. The results highlight the superiority of multi-objective optimization in achieving Nash-type Pareto compromise solution for sustainable concrete design, offering a principled and interpretable framework for high-performance, eco-efficient mixes.
Key Words
CO2 Emission; Environment; Game Theory; Genetic Algorithm (GA); Multi-Objectives Optimization; Nash-Type Compromise Solution; Nsga-ii; Random Forest (RF); Sustainable Concrete Mixture
Address
(1) Razan Haedar Al Marahla:
Civil Engineering Department, Al-Zaytoonah University of Jordan, Amman, Jordan;
(2) Mohammad Zakaria Masoud:
Electrical Engineering Department, Al-Zaytoonah University of Jordan, Amman, Jordan;
(3) Ayman Mohammad Nasir:
Mechanical Engineering Department, Al-Zaytoonah University of Jordan, Amman, Jordan.
| This page lists Virtual Special Issue articles for Smart Structures and Systems. |
open access 📚 Virtual Special Issue