Faster Region Convolutional Neural Network(FRCNN)Based Facial Emotion Recognition

Facial emotion recognition(FER)has become a focal point of research due to its widespread applications,ranging from human-computer interaction to affective computing.While traditional FER techniques have relied on handcrafted features and classification models trained on image or video datasets,recent strides in artificial intelligence and deep learning(DL)have ushered in more sophisticated approaches.The research aims to develop a FER system using a Faster Region Convolutional Neural Network(FRCNN)and design a specialized FRCNN architecture tailored for facial emotion recognition,leveraging its ability to capture spatial hierarchies within localized regions of facial features.The proposed work enhances the accuracy and efficiency of facial emotion recognition.The proposed work comprises twomajor key components:Inception V3-based feature extraction and FRCNN-based emotion categorization.Extensive experimentation on Kaggle datasets validates the effectiveness of the proposed strategy,showcasing the FRCNN approach’s resilience and accuracy in identifying and categorizing facial expressions.The model’s overall performance metrics are compelling,with an accuracy of 98.4%,precision of 97.2%,and recall of 96.31%.This work introduces a perceptive deep learning-based FER method,contributing to the evolving landscape of emotion recognition technologies.The high accuracy and resilience demonstrated by the FRCNN approach underscore its potential for real-world applications.This research advances the field of FER and presents a compelling case for the practicality and efficacy of deep learning models in automating the understanding of facial emotions.

基于MLF-FRCNN的高速列车定位器检测技术研究

随着我国高速列车的快速发展,提高接触网系统的检测技术具有重要的现实意义。定位器是接触网系统的关键零部件之一,其良好的坡度状态确保受电弓具有高的受流质量。传统的定位器检测主要采用人工巡检方式或者基于机器学习的图像检测技术。它们由于检测效率太低而逐渐不满足铁路大时代下高精确率和高实时性的检测实际需求。由于近年来深度学习和计算机视觉技术的提高和广泛应用,针对上述问题论文提出基于改进型的Faster R-CNN定位器视觉检测系统。所提出的改进型Faster R-CNN深度学习模型创新性地利用多尺度卷积特征,融合了低级和高级的卷积特征,因此称它为多尺度特征Faster R-CNN(Multi-Level Features Faster R-CNN,MLF-FRCNN)。对高速列车检测系统采集的某线路接触网图像进行实验分析,其结果表明所提出的基于MLF-FRCNN的定位器视觉检测模型具有很高的精确率和实时性。这为后期定位器的维修保养工作奠定了基础,同时对保障高速列车的安全运行具有重要的意义。

基于深度学习的配电网工程数据融合与智能分析方法研究

配网工程数据的快速增长给数据的处理与分析带来了新的挑战。针对配网工程中数据量大、信息处理困难等问题,开展了基于深度学习的配网工程数据融合与智能分析方法研究。通过建立改进的FP-FRCNN检测模型以及压缩连接金字塔结构,进行池化层调整。采用全智能分析技术进行大数据降噪处理,利用聚类分析的稀疏自编码数据融合算法进行数据融合,将配网终端采集到的现场数据进行数据挖掘及可视化分析。以某区域配网工程数据来进行算例分析,实验结果表明,文中所提方法在不同分辨率下的AP值达到了95.6%,平均相对误差为3.53%。