This paper presents two systems for recognizing static signs (digits) from American Sign Language (ASL). These systems avoid the use color marks, or gloves, using instead, low-pass and high-pass filters in space and f...This paper presents two systems for recognizing static signs (digits) from American Sign Language (ASL). These systems avoid the use color marks, or gloves, using instead, low-pass and high-pass filters in space and frequency domains, and color space transformations. First system used rotational signatures based on a correlation operator;minimum distance was used for the classification task. Second system computed the seven Hu invariants from binary images;these descriptors fed to a Multi-Layer Perceptron (MLP) in order to recognize the 9 different classes. First system achieves 100% of recognition rate with leaving-one-out validation and second experiment performs 96.7% of recognition rate with Hu moments and 100% using 36 normalized moments and k-fold cross validation.展开更多
Considering that real communication signals corrupted by noise are generally nonstationary, and timefrequency distributions are especially suitable for the analysis of nonstationary signals, time-frequency distributio...Considering that real communication signals corrupted by noise are generally nonstationary, and timefrequency distributions are especially suitable for the analysis of nonstationary signals, time-frequency distributions are introduced for the modulation classification of communication signals： The extracted time-frequency features have good classification information, and they are insensitive to signal to noise ratio （SNR） variation. According to good classification by the correct rate of a neural network classifier, a multilayer perceptron （MLP） classifier with better generalization, as well as, addition of time-frequency features set for classifying six different modulation types has been proposed. Computer simulations show that the MLP classifier outperforms the decision-theoretic classifier at low SNRs, and the classification experiments for real MPSK signals verify engineering significance of the MLP classifier.展开更多
This paper focuses on some application issues in m.multi-layered perceptrons researches. The following problem areas are discussed: (1) the classification capability of multi-layered perceptrons; (2) theself-configura...This paper focuses on some application issues in m.multi-layered perceptrons researches. The following problem areas are discussed: (1) the classification capability of multi-layered perceptrons; (2) theself-configuration algorithm for facilitating the design of the neural nets' structure;and,finally (3) the application of the fast BP algorithm to speed up the learning procedure. Some experimental results with respect to the application of multi-layered perceptrons as classifier systems in the comprehensive evaluation of Chinese large cities are presented.展开更多
核反应堆中极高参数条件下换热系数(Coefficient of Heat Transfer,HTC)的准确预测对反应堆的设计及运行至关重要,但因涉及不同流型的多重因素影响的复杂情形,物理机理仍不完全明晰。由于缺乏满足实际反应堆高温高压下的参数实验数据,...核反应堆中极高参数条件下换热系数(Coefficient of Heat Transfer,HTC)的准确预测对反应堆的设计及运行至关重要,但因涉及不同流型的多重因素影响的复杂情形,物理机理仍不完全明晰。由于缺乏满足实际反应堆高温高压下的参数实验数据,而严重依赖实验数据的半经验关系式很难满足核反应堆高精度数值计算的要求。深度学习算法能够有效预测和解决复杂的非线性问题,但存在外推性能差以及过拟合等不足。本研究采用先验物理信息Jens-Lottes关系式、Thom关系式与机器学习算法中多层感知机(Multi-layer Perceptron,MLP)、反向传播神经网络(Backpropagation Neural Network,BPNN)和随机森林(Random Forest,RF)相结合的方式开发HTC预测模型,基于圆管通道HTC实验数据训练神经网络并进行验证,对6种不同的物理信息机器学习(Physical Information Machine Learning,PIML)算法模型的适用性以及预测精度进行评估。结果表明:(1)基于Jens-Lottes关系式与RF相结合的模型为最佳预测模型,对实验数据的预测平均相对误差为3.17%,且模型可扩展范围占总适用范围的63.6%,具有良好的外推适用性(;2)使用基于物理信息机器学习算法能够有效提高关系式的计算准确度,基于Jens-Lottes关系式与RF相结合的模型相比于经验关系式评价相对误差降低了24.5%。本研究结果为说明采用物理信息机器学习算法对核反应堆热工参数经验关系式的计算可提高精度并扩大适用范围提供了参考依据。展开更多
目的生成式和传统人工智能模型是信息时代的关键工具。在这些技术的助力下,眼动过程的样本生成与识别显得尤为关键,它已成为深入研究认知机制的重要手段。为了推动生成式人工智能在眼动技术领域的应用发展,解决眼动样本生成及因网络深...目的生成式和传统人工智能模型是信息时代的关键工具。在这些技术的助力下,眼动过程的样本生成与识别显得尤为关键,它已成为深入研究认知机制的重要手段。为了推动生成式人工智能在眼动技术领域的应用发展,解决眼动样本生成及因网络深度增加而导致的不透明性和不可解释性问题,并深入挖掘与幼儿语言发展相关的眼动数据,方法采集4~6岁幼儿理解不同焦点结构的眼动数据,采用生成式人工智能模型-变分自编码器(variational autoencoder,VAE)和传统模型-多层感知器(multi-layer perceptron,MLP)识别眼动模式的发展差异并尝试生成新样本,基于灰色关联分析和混淆矩阵对生成式数据集进行解释。结果结果表明:(1)VAE生成的4岁组、5岁组和6岁组幼儿眼动数据集精度高于MINIST数据集(mixed National Institute of Standards and Technology database),且与MLP分析结果一致,具有准确性、多样性和一定的可解释性;(2)生成式眼动数据及混淆矩阵结果表明,在无焦点结构句式中,幼儿在4~5岁、5~6岁两个阶段理解水平均有提升,而宾语焦点结构和主语焦点结构的眼动特征在4~5岁变化较小,5~6岁变化较大,说明幼儿对焦点结构的理解在5岁是一个关键期,这符合幼儿焦点结构理解发展规律。结论提出的人工智能耦合分析方法,具备有效识别眼动特征发展模式的能力,并能据此生成可靠的新样本。这一方法不仅为生成式人工智能与眼动技术的融合开辟了新的途径,而且为复杂语言理解问题提供了全新的思考方向。展开更多
文摘This paper presents two systems for recognizing static signs (digits) from American Sign Language (ASL). These systems avoid the use color marks, or gloves, using instead, low-pass and high-pass filters in space and frequency domains, and color space transformations. First system used rotational signatures based on a correlation operator;minimum distance was used for the classification task. Second system computed the seven Hu invariants from binary images;these descriptors fed to a Multi-Layer Perceptron (MLP) in order to recognize the 9 different classes. First system achieves 100% of recognition rate with leaving-one-out validation and second experiment performs 96.7% of recognition rate with Hu moments and 100% using 36 normalized moments and k-fold cross validation.
文摘Considering that real communication signals corrupted by noise are generally nonstationary, and timefrequency distributions are especially suitable for the analysis of nonstationary signals, time-frequency distributions are introduced for the modulation classification of communication signals： The extracted time-frequency features have good classification information, and they are insensitive to signal to noise ratio （SNR） variation. According to good classification by the correct rate of a neural network classifier, a multilayer perceptron （MLP） classifier with better generalization, as well as, addition of time-frequency features set for classifying six different modulation types has been proposed. Computer simulations show that the MLP classifier outperforms the decision-theoretic classifier at low SNRs, and the classification experiments for real MPSK signals verify engineering significance of the MLP classifier.
文摘This paper focuses on some application issues in m.multi-layered perceptrons researches. The following problem areas are discussed: (1) the classification capability of multi-layered perceptrons; (2) theself-configuration algorithm for facilitating the design of the neural nets' structure;and,finally (3) the application of the fast BP algorithm to speed up the learning procedure. Some experimental results with respect to the application of multi-layered perceptrons as classifier systems in the comprehensive evaluation of Chinese large cities are presented.
文摘目的生成式和传统人工智能模型是信息时代的关键工具。在这些技术的助力下,眼动过程的样本生成与识别显得尤为关键,它已成为深入研究认知机制的重要手段。为了推动生成式人工智能在眼动技术领域的应用发展,解决眼动样本生成及因网络深度增加而导致的不透明性和不可解释性问题,并深入挖掘与幼儿语言发展相关的眼动数据,方法采集4~6岁幼儿理解不同焦点结构的眼动数据,采用生成式人工智能模型-变分自编码器(variational autoencoder,VAE)和传统模型-多层感知器(multi-layer perceptron,MLP)识别眼动模式的发展差异并尝试生成新样本,基于灰色关联分析和混淆矩阵对生成式数据集进行解释。结果结果表明:(1)VAE生成的4岁组、5岁组和6岁组幼儿眼动数据集精度高于MINIST数据集(mixed National Institute of Standards and Technology database),且与MLP分析结果一致,具有准确性、多样性和一定的可解释性;(2)生成式眼动数据及混淆矩阵结果表明,在无焦点结构句式中,幼儿在4~5岁、5~6岁两个阶段理解水平均有提升,而宾语焦点结构和主语焦点结构的眼动特征在4~5岁变化较小,5~6岁变化较大,说明幼儿对焦点结构的理解在5岁是一个关键期,这符合幼儿焦点结构理解发展规律。结论提出的人工智能耦合分析方法,具备有效识别眼动特征发展模式的能力,并能据此生成可靠的新样本。这一方法不仅为生成式人工智能与眼动技术的融合开辟了新的途径,而且为复杂语言理解问题提供了全新的思考方向。
