Theory-guided deep neural network for boiler 3-D NOx concentration distribution prediction

Tang, Zhenhao; Sui, Mengxuan; Wang, Xu; Xue, Wenyuan; Yang, Yuan; Wang, Zhi; Ouyang, Tinghui

doi:10.1016/j.energy.2024.131500

Cited by 18 publications

(1 citation statement)

References 38 publications

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“…Amidst the big data era, the challenges brought by high-dimensional data in areas like machine learning, data compression, pattern recognition, and data visualization have emerged as key areas of focus [1,2]. Especially in advanced pattern recognition domains like facial recognition, the escalation in data dimensions leads to an exponential increase in the needed sample size for efficacious statistical learning and feature identification [3,4]. As a result, the challenge of effectively learning and processing with limited high-dimensional samples (such as facial images with various angles and expressions) for more precise facial classification, feature denoising, and identity tracking has become a focal point of research.…”

Section: Introductionmentioning

confidence: 99%

Integration of Manifold Learning and Density Estimation for Fine-Tuned Face Recognition

Ge,

Zhu,

Ouyang

et al. 2024

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With the rapid advancements in data analysis and the increasing complexity of high-dimensional datasets, traditional dimensionality reduction techniques like Local Linear Embedding (LLE) often face challenges in maintaining accuracy and efficiency. This research aims to overcome the limitations of LLE, specifically its reliance on the nearest neighbor concept, its inability to distinguish differences among manifold points, and its underutilization of data discrimination information. To address these issues, we propose an advanced LLE algorithm that integrates decision tree-based neighbor recognition with Gaussian kernel density estimation. Decision trees accurately determine neighboring relationships, which are then optimized using Gaussian kernel density estimation to better reflect the distribution of sample points on the manifold. The algorithm also incorporates data discrimination information to enhance classification accuracy and efficiency. Evaluations in facial recognition tasks using SVM classifiers demonstrate significant improvements. Integrating decision trees (LLE-DT) yielded accuracy gains, with LFW at 98.75%, CFP 96.10%, and Olivetti 92.18%. Gaussian density estimation (LLE-GDE) achieved further enhancements, especially in LFW (99.13%), with CFP at 96.85%, and Olivetti at 91.82%. Combining both methods (LLE-DT-GDE) led to substantial improvements: LFW 99.61%, CFP 97.23%, and Olivetti 93.56%, highlighting the synergy between decision trees and Gaussian estimation. This advanced LLE algorithm effectively addresses the limitations of traditional approaches, showing promising results in complex data processing tasks such as facial recognition. These findings suggest its potential for broader applications in fields requiring robust data analysis and classification.

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Section: Introductionmentioning

confidence: 99%