Current Issue : April-June Volume : 2026 Issue Number : 2 Articles : 5 Articles
Laser engraving may be used in a variety of industries, from medicine to defense, and it has many uses that require high-quality precision production. However, in practice, operators have to adjust the laser settings manually, which can result in wasted material and poor color quality and even decrease productivity. Current optimization approaches mostly concentrate on single objectives, making it impossible to co-optimize engraving quality and production efficiency simultaneously. In this paper, an approach based on a multiobjective genetic algorithm, a combination of NSGA-II, SPEA2, and MOEA/D, is proposed to automatically establish the relationship between CMYK color attributes, which are extracted from images of engravings, and laser parameters (power, speed, and frequency). Anodized aluminum 6061 was laser-processed using an SPI 30W fiber laser. While the proposed framework is general, the experimental validation in this study was specifically constrained to this material. The results also indicate that MOEA/D converges in a short time and becomes relatively stable after 20 generations. NSGA-II results in solutions that are more diverse, and SPEA2 offers a good trade-off between the speed of convergence and solution size. This approach resulted in optimization in terms of both a decrease in material used and color matching between manual operations, with the average CMYK improvement being up to 28%. Our results indicate that multi-objective evolutionary optimization is feasible for the optimization of efficiency and quality in laser cutting....
The design, control, simulation and animation of robotic systems heavily depend on dynamic modeling. A variety of studies have explored different dynamic modeling methodologies applied to diverse robotic mechanisms. Artificial neural networks (ANNs) have proven their value in engineering design in recent years, enhancing the understanding of complex mechanisms as well as shortening experimental periods and decreasing related expenses. This study investigates the application of various neural network algorithms for the analysis of a custom-designed three-link planar revolute–prismatic–revolute (RPR) robotic arm mechanism. Initially, the Euler–Lagrange equations of motion for the RPR mechanism are derived. Joint accelerations are then computed under different mass configurations of the robotic links, resulting in a dataset comprising 204 joint acceleration samples. Six distinct neural network models are subsequently employed to perform regression analysis on the collected data. The primary objective of this study is to analyze the relationship between joint accelerations and varying link masses under constant joint torques and forces, while its secondary aim is to present a representative application of neural networks as regression learners for the dynamic modeling of robotic mechanisms. The approach outlined in this study allows users to select appropriate neural network algorithms for use in specific applications, considering the wide range of available algorithms. Link mass variations and their effects on joint accelerations are investigated, establishing a basis for the modeling of robotic dynamics using regression-based neural networks. The results indicate that the optimizable neural network algorithm produces the best regression accuracy results, although the other models maintain similar performance levels....
This paper presents the Gaussian conditional method (GCM) for the problem of frequency difference of arrival (FDOA)-only source localization under correlated noise. GCM identifies the source position through approximating its posterior distribution using a Gaussian mixture model (GMM) and applying successive conditioning to the measurement likelihood. The algorithm development leverages the fact that FDOA measurements follow a multivariate Gaussian distribution with a non-diagonal covariance. Simulation results demonstrate that GCM can achieve the Cramér–Rao lower bound (CRLB) under moderate noise levels, while having lower computational complexity than baseline techniques including the recently developed Gaussian division method (GDM). The proposed algorithm is particularly effective for passively locating narrowband sources, where the time difference of arrival (TDOA) measurements become unreliable, and it can operate without the need for accurate initialization....
This paper introduces a novel observer-based, fully distributed fault-tolerant consensus control algorithm for model-free adaptive control, specifically designed to tackle the consensus problem in nonlinear multi-agent systems. The method addresses the issue of followers lacking direct access to the leader’s state by employing a distributed observer that estimates the leader’s state using only local information from the agents. This transforms the consensus control challenge into multiple independent tracking tasks, where each agent can independently follow the leader’s trajectory. Additionally, an extended state observer based on a data-driven model is utilized to estimate unknown actuator faults, with a particular focus on brake faults. Integrated into the model-free adaptive control framework, this observer enables real-time fault detection and compensation. The proposed algorithm is supported by rigorous theoretical analysis, which ensures the boundedness of both the observer and tracking errors. Simulation results further validate the algorithm’s effectiveness, demonstrating its robustness and practical viability in real-time fault-tolerant control applications....
Algorithm- driven platforms have become central to modern consumer decision- making. While consumers have always been subject to bounded rationality, recent discussion suggests that personalized recommendation systems can further constrain consumer choices and, in turn, reinforce various cognitive biases. This article explores the theoretical underpinnings of bounded rationality in the context of algorithmically curated content, outlines key biases that are exacerbated by these systems, and discusses the societal implications of algorithmic confinement—from political polarization to consumer rights concerns. Finally, it proposes potential solutions, including policy- based interventions, forced diversity mechanisms, and consumer education approaches that can mitigate the adverse effects of algorithm- driven recommendation systems....
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