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. 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. 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. 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. Different types of data need to undergo
appropriate pre-processing and feature extraction operations to
better represent the information.
Loop layer: The loop layer utilizes RNN to
capture the sequence information of the input data. Commonly used
RNN units include long short-term memory (LSTM) and gated recurrent
unit (GRU). Through recurrent connections, the RNN can model the
input sequence and pass the historical information to the
subsequent layers.
Convolutional layer: The convolutional layer
utilizes CNN to extract local features of the input data. Through
convolution operation and pooling operation, CNN can effectively
capture spatial and temporal correlations in the input data. The
convolutional layer is usually used to process image data or
spectral representation of speech data.
Fusion layer: The fusion layer fuses the outputs
of the recurrent and convolutional layers to obtain more
comprehensive and enriched features, and the fused features are fed
into the next layer.
Output layer: The output layer is designed
according to the specific task, for example, the classification
task can use a fully connected layer and softmax function for
multi-category classification. The result of the output layer can
represent the category or probability distribution of the
human-robot collaborative intent.
Using hybrid recurrent neural network
architecture for human-robot collaboration intention recognition
can greatly improve the efficiency and quality of human-robot
collaboration. Human-robot collaboration intention recognition is
an important research area that can help robots to better
understand human intentions and goals, thus enabling more
intelligent and efficient human-robot collaboration. By accurately
understanding human intentions, robots can better respond to and
assist humans in accomplishing tasks, thus improving work
efficiency. In addition, human-robot collaboration intent
recognition can improve the user experience of human-robot
interaction. If robots can accurately recognize the human's
intention and respond accordingly, the user will feel more natural
and comfortable, thus enhancing the user's trust and satisfaction
with robots. Human-robot collaborative intent recognition can be
applied in various fields, such as smart home, smart office, and
smart healthcare, etc., to bring convenience and benefits to
people's lives and work.
In the field of hybrid recurrent neural network
architecture-based human-robot collaboration intention recognition,
there are other research directions that deserve further
exploration. Current human-robot collaboration intention
recognition mainly relies on text data, but actual human-robot
interaction often involves multi-modal information, such as speech,
image, video, etc. WiMi will try to fuse multi-modal information
into a hybrid recurrent neural network architecture-based
human-robot collaboration intention recognition. In the future,
WiMi will try to fuse multi-modal information into a hybrid
recurrent neural network and utilize migration learning to enhance
human-robot collaborative intent recognition, and continuously
expand the application scope of human-robot collaborative intent
recognition through further research and exploration.
About WIMI Hologram CloudWIMI Hologram Cloud,
Inc. (NASDAQ:WIMI) is a holographic cloud comprehensive technical
solution provider that focuses on professional areas including
holographic AR automotive HUD software, 3D holographic pulse LiDAR,
head-mounted light field holographic equipment, holographic
semiconductor, holographic cloud software, holographic car
navigation and others. Its services and holographic AR technologies
include holographic AR automotive application, 3D holographic pulse
LiDAR technology, holographic vision semiconductor technology,
holographic software development, holographic AR advertising
technology, holographic AR entertainment technology, holographic
ARSDK payment, interactive holographic communication and other
holographic AR technologies.
Safe Harbor StatementsThis press release
contains "forward-looking statements" within the Private Securities
Litigation Reform Act of 1995. These forward-looking statements can
be identified by terminology such as "will," "expects,"
"anticipates," "future," "intends," "plans," "believes,"
"estimates," and similar statements. Statements that are not
historical facts, including statements about the Company's beliefs
and expectations, are forward-looking statements. Among other
things, the business outlook and quotations from management in this
press release and the Company's strategic and operational plans
contain forward−looking statements. The Company may also make
written or oral forward−looking statements in its periodic reports
to the US Securities and Exchange Commission ("SEC") on Forms 20−F
and 6−K, in its annual report to shareholders, in press releases,
and other written materials, and in oral statements made by its
officers, directors or employees to third parties. Forward-looking
statements involve inherent risks and uncertainties. Several
factors could cause actual results to differ materially from those
contained in any forward−looking statement, including but not
limited to the following: the Company's goals and strategies; the
Company's future business development, financial condition, and
results of operations; the expected growth of the AR holographic
industry; and the Company's expectations regarding demand for and
market acceptance of its products and services.
Further information regarding these and other
risks is included in the Company's annual report on Form 20-F and
the current report on Form 6-K and other documents filed with the
SEC. All information provided in this press release is as of the
date of this press release. The Company does not undertake any
obligation to update any forward-looking statement except as
required under applicable laws.
ContactsWIMI Hologram Cloud Inc.Email:
pr@wimiar.comTEL: 010-53384913
ICR, LLCRobin YangTel: +1 (646) 975-9495Email:
wimi@icrinc.com
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