Simplicial Homology Global Optimisation in the Problem of Point-to-Point Ionospheric Ray Tracing

Tereshin, N. A.; Padokhin, A. M.; Andreeva, Elena; Kozlovtseva, E. A.

doi:10.23919/ursigass49373.2020.9232337

Cited by 2 publications

(4 citation statements)

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Section: Simplicial Homology Global Optimizationmentioning

confidence: 99%

“…The SHGO algorithm is a powerful and versatile optimization technique that has demonstrated its effectiveness in a wide range of optimization problems [63][64][65][66]. One of the key benefits of SHGO is its adaptability to changing search spaces, making it ideal for dynamic optimization problems [65]. Additionally, its level of self-learning enables it to improve its performance over time, while maintaining remarkable scalability in terms of both the number of dimensions and the number of candidate solutions explored.…”

Section: Simplicial Homology Global Optimizationmentioning

confidence: 99%

“…The stochastic search strategy evaluates the current search potential using techniques such as simulated annealing, genetic algorithms, ant colony optimization, and ant-inspired techniques. The mathematical principles of the SHGO algorithm are simplicial integral homology and combinatorial topology [65,67]. Black box optimization refers to optimizing an objective function without knowing its inner workings and only relying on inputoutput behavior.…”

Section: Simplicial Homology Global Optimizationmentioning

confidence: 99%

“…Due to the efficiency by which the SHGO quickly locates all local minima of an objective function [64,65], and the black box nature of brain EEG signals' extraction process, the algorithm is particularly well-suited for selecting the best combination of EEG electrodes and the associated frequency ranges for recognizing human emotions.…”

Section: Simplicial Homology Global Optimizationmentioning

confidence: 99%

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Simplicial Homology Global Optimization of EEG Signal Extraction for Emotion Recognition

et al. 2023

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Emotion recognition is a vital part of human functioning. textcolorredIt enables individuals to respond suitably to environmental events and develop self-awareness. The fast-paced developments in brain–computer interfacing (BCI) technology necessitate that intelligent machines of the future be able to digitize and recognize human emotions. To achieve this, both humans and machines have relied on facial expressions, in addition to other visual cues. While facial expressions are effective in recognizing emotions, they can be artificially replicated and require constant monitoring. In recent years, the use of Electroencephalography (EEG) signals has become a popular method for emotion recognition, thanks to advances in deep learning and machine learning techniques. EEG-based systems for recognizing emotions involve measuring electrical activity in the brain of a subject who is exposed to emotional stimuli such as images, sounds, or videos. Machine learning algorithms are then used to extract features from the electrical activity data that correspond to specific emotional states. The quality of the extracted EEG signal is crucial, as it affects the overall complexity of the system and the accuracy of the machine learning algorithm. This article presents an approach to improve the accuracy of EEG-based emotion recognition systems while reducing their complexity. The approach involves optimizing the number of EEG channels, their placement on the human scalp, and the target frequency band of the measured signal to maximize the difference between high and low arousal levels. The optimization method, called the simplicial homology global optimization (SHGO), is used for this purpose. Experimental results demonstrate that a six-electrode configuration optimally placed can achieve a better level of accuracy than a 14-electrode configuration, resulting in an over 60% reduction in complexity in terms of the number of electrodes. This method demonstrates promising results in improving the efficiency and accuracy of EEG-based emotion recognition systems, which could have implications for various fields, including healthcare, psychology, and human–computer interfacing.

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Section: Simplicial Homology Global Optimizationmentioning

confidence: 99%