Coati optimized FOPID controller for non‐isolated DC–DC converters in EV charging application

IET Power Electronics, Год журнала: 2024, Номер unknown

Опубликована: Окт. 17, 2024

Abstract The transportation sector's shift from internal combustion engines to electric vehicles (EVs) has made enough charging facilities necessary. converter's architecture undergone several changes provide the best possible for vehicles. For EV applications, both isolated and non‐isolated converters are employed. significant strain on switches losses in various converter topologies among main problems. To minimize these issues, current‐fed DC–DC is proposed with fewer switching devices. design validated application MATLAB/Simulink tool. Moreover, Coati optimized fractional order proportional integral derivative controller proposed, which provides optimum signals based voltage input. Furthermore, responses realized buck boost modes of operations. It verified that zero current achieved under mode. results analysis demonstrates a higher efficiency 99.7% 99.02% mode, respectively.

Язык: Английский

---

Enhancing local representation learning through global–local integration with functional connectivity for EEG-based emotion recognition

Computers in Biology and Medicine, Год журнала: 2024, Номер 179, С. 108857 - 108857

Опубликована: Июль 17, 2024

Язык: Английский

---

Mathematical modeling of a Hybrid Mutated Tunicate Swarm Algorithm for Feature Selection and Global Optimization

AIMS Mathematics, Год журнала: 2024, Номер 9(9), С. 24336 - 24358

Опубликована: Янв. 1, 2024

<p>The latest advances in engineering, science, and technology have contributed to an enormous generation of datasets. This vast dataset contains irrelevant, redundant, noisy features that adversely impact classification performance data mining machine learning (ML) techniques. Feature selection (FS) is a preprocessing stage minimize the dimensionality by choosing most prominent feature while improving performance. Since size produced are often extensive dimension, this enhances complexity search space, where maximal number potential solutions 2nd for n As becomes large, it computationally impossible compute feature. Therefore, there need effective FS techniques large-scale problems classification. Many metaheuristic approaches were utilized resolve challenges heuristic-based approaches. Recently, swarm algorithm has been suggested demonstrated perform effectively tasks. I developed Hybrid Mutated Tunicate Swarm Algorithm Global Optimization (HMTSA-FSGO) technique. The proposed HMTSA-FSGO model mainly aims eradicate unwanted choose relevant ones highly classifier results. In model, HMTSA derived integrating standard TSA with two concepts: A dynamic s-best mutation operator optimal trade-off between exploration exploitation directional rule enhanced space exploration. also includes bidirectional long short-term memory (BiLSTM) examine process. rat optimizer (RSO) can hyperparameters boost BiLSTM network simulation analysis technique tested using series experiments. investigational validation showed superior outcome 93.01%, 97.39%, 61.59%, 99.15%, 67.81% over diverse datasets.</p>

Язык: Английский

---

Emotion analysis and recognition in 3D space using classifier-dependent feature selection in response to tactile enhanced audio–visual content using EEG

Computers in Biology and Medicine, Год журнала: 2024, Номер 179, С. 108807 - 108807

Опубликована: Июль 5, 2024

Язык: Английский

---

G-EEGCS: Graph-based optimum electroencephalogram channel selection

Biomedical Signal Processing and Control, Год журнала: 2024, Номер 98, С. 106763 - 106763

Опубликована: Авг. 24, 2024

Язык: Английский

---