Beijing, Dec. 18, 2023 (GLOBE NEWSWIRE) -- WiMi Hologram Cloud Inc. (NASDAQ: WIMI) ('WiMi' or the 'Company'), a leading global Hologram Augmented Reality ('AR') Technology provider, today announced that it proposed hybrid recurrent neural network architecture-based human-robot collaboration intent recognition.

北京，2023年12月18日（环球通讯社）——全球领先的全息增强现实（AR）技术提供商WiMi全息云公司（纳斯达克：WiMi）（“WiMi”或“公司”）今天宣布，它提出了基于混合递归神经网络架构的人-机器人协作意图识别。

Hybrid recurrent neural network architecture is a model that combines recurrent neural network (RNN) and convolutional neural network (CNN). RNN is a neural network suitable for modeling and sequential data processing, which can efficiently capture temporal information and contextual relationships in the data through recurrent connections and hidden state updating, it can effectively capture temporal information and contextual relationships in sequence data.

混合递归神经网络结构是将递归神经网络（RNN）和卷积神经网络（CNN）相结合的模型。RNN是一种适用于建模和序列数据处理的神经网络，它可以通过递归连接和隐藏状态更新有效地捕获数据中的时间信息和上下文关系，它可以有效地捕获序列数据中的时间信息和上下文关系。

CNN can effectively extract data features. Hybrid recurrent neural network combines the advantages of RNN and CNN, which can better capture sequence information and local features, and can better handle intention recognition for human-robot collaboration. In hybrid recurrent neural network architecture, the input data is first subjected to feature extraction by CNN, then temporal modeling by recurrent layer, and then mapping the features to the intent by a fully connected layer.

CNN可以有效地提取数据特征。混合递归神经网络结合了RNN和CNN的优点，可以更好地捕获序列信息和局部特征，并且可以更好地处理人机协作的意图识别。在混合递归神经网络结构中，输入数据首先通过CNN进行特征提取，然后通过递归层进行时间建模，然后通过完全连接层将特征映射到意图。

During the training process, the backpropagation algorithm is used to optimize the model parameters to improve the accuracy of intent recognition. WiMi's hybrid recurrent neural network architecture-based human-robot collaboration intent recognition mainly consists of: Input layer: The input layer receives raw data from the human-robot collaborative scenario, such as speech, images, or text.

在训练过程中，使用反向传播算法优化模型参数，以提高意图识别的准确性。WiMi基于混合递归神经网络架构的人-机器人协作意图识别主要由以下部分组成：输入层：输入层接收来自人-机器人协作场景的原始数据，例如语音，图像或文本。

Different types of data need to undergo appropriate pre-processing and feature extraction operations to better represent the information. Loop l.

不同类型的数据需要进行适当的预处理和特征提取操作，以更好地表示信息。回路l。