In this paper, we tackle the problem of Unmanned Aerial (UA V) path planning
in complex and uncertain environments by designing a Model Predictive Control
(MPC), based on a Long-Short-Term Memory (LSTM) network integrated into the
Deep Deterministic Policy Gradient algorithm. In the proposed solution,
LSTM-MPC operates as a deterministic policy within the DDPG network, and it
leverages a predicting pool to store predicted future states and actions for
improved robustness and efficiency. The use of the predicting pool also enables
the initialization of the critic network, leading to improved convergence speed
and reduced failure rate compared to traditional reinforcement learning and
deep reinforcement learning methods. The effectiveness of the proposed solution
is evaluated by numerical simulations.

本文提出一种基于深度学习算法的无人机航迹规划方法，采用 LSTM-MPC 作为决策策略，并结合预测池来提高系统鲁棒性和效率，同时通过数值仿真实验验证了该方法的有效性。

应用 MPC - 强化学习方法的无人机路径规划考虑避障

UAV Path Planning Employing MPC- Reinforcement Learning Method Considering Collision Avoidance

We develop Few-Shot Learning models trained to recognize five or ten
different dynamic hand gestures, respectively, which are arbitrarily
interchangeable by providing the model with one, two, or five examples per hand
gesture. All models were built in the Few-Shot Learning architecture of the
Relation Network (RN), in which Long-Short-Term Memory cells form the backbone.
The models use hand reference points extracted from RGB-video sequences of the
Jester dataset which was modified to contain 190 different types of hand
gestures. Result show accuracy of up to 88.8% for recognition of five and up to
81.2% for ten dynamic hand gestures. The research also sheds light on the
potential effort savings of using a Few-Shot Learning approach instead of a
traditional Deep Learning approach to detect dynamic hand gestures. Savings
were defined as the number of additional observations required when a Deep
Learning model is trained on new hand gestures instead of a Few Shot Learning
model. The difference with respect to the total number of observations required
to achieve approximately the same accuracy indicates potential savings of up to
630 observations for five and up to 1260 observations for ten hand gestures to
be recognized. Since labeling video recordings of hand gestures implies
significant effort, these savings can be considered substantial.

本研究使用 Few-Shot Learning 模型，通过提供一到五个手势示例来识别五到十种不同的动态手势，模型的主干是使用 Long-Short-Term Memory 组成的 Relation Network，使用了 Jester 数据集中提取的手势参考点进行训练，在动态手势的识别方面的准确率为五种手势高达 88.8%，十种手势高达 81.2%，并尝试对 Few-Shot Learning 方法和传统 Deep Learning 方法之间对比，并表现出了大量观察数量的巨大节省潜力。

使用少量样本学习模型快速学习动态手势识别

Fast Learning of Dynamic Hand Gesture Recognition with Few-Shot Learning Models

This paper realizes the estimation of classroom occupancy by using the CO2
sensor and deep learning technique named Long-Short-Term Memory. As a case of
connection with IoT and machine learning, I achieve the model to estimate the
people number in the classroom based on the environmental data exported from
the CO2 sensor, I also evaluate the performance of the model to show the
feasibility to apply our module to the real environment.

利用 CO2 传感器和长短时记忆深度学习技术，估算教室的人数，模型表现良好，适用于实际环境。