The machine vision systems have been playing a significant role in visual
monitoring systems. With the help of stereovision and machine learning, it will
be able to mimic human-like visual system and behaviour towards the
environment. In this paper, we present a stereo vision based 3-DOF robot which
will be used to monitor places from remote using cloud server and internet
devices. The 3-DOF robot will transmit human-like head movements, i.e., yaw,
pitch, roll and produce 3D stereoscopic video and stream it in Real-time. This
video stream is sent to the user through any generic internet devices with VR
box support, i.e., smartphones giving the user a First-person real-time 3D
experience and transfers the head motion of the user to the robot also in
Real-time. The robot will also be able to track moving objects and faces as a
target using deep neural networks which enables it to be a standalone
monitoring robot. The user will be able to choose specific subjects to monitor
in a space. The stereovision enables us to track the depth information of
different objects detected and will be used to track human interest objects
with its distances and sent to the cloud. A full working prototype is developed
which showcases the capabilities of a monitoring system based on stereo vision,
robotics, and machine learning.

基于立体视觉、机器人和机器学习的监控系统，通过云服务器和互联网设备，实时跟踪和监测移动物体和人脸等特定目标，提供给用户具有立体感的第一人称实时 3D 体验。

基于立体视觉的具备 VR 支持的远程监控机器人

Stereo Vision Based Robot for Remote Monitoring with VR Support

Intelligent robots need to generate and execute plans. In order to deal with
the complexity of real environments, planning makes some assumptions about the
world. When executing plans, the assumptions are usually not met. Most works
have focused on the negative impact of this fact and the use of replanning
after execution failures. Instead, we focus on the positive impact, or
opportunities to find better plans. When planning, the proposed technique finds
and stores those opportunities. Later, during execution, the monitoring system
can use them to focus perception and repair the plan, instead of replanning
from scratch. Experiments in several paradigmatic robotic tasks show how the
approach outperforms standard replanning strategies.

智能机器人在规划和执行计划过程中，通过监测系统寻找机会，以解决复杂环境中执行失败的问题，并超过常规的重新规划策略。

智能执行计划分析系统

Intelligent Execution through Plan Analysis

A core part of maintenance planning is a monitoring system that provides a
good prognosis on health and degradation, often expressed as remaining useful
life (RUL). Most of the current data-driven approaches for RUL prediction focus
on single-point prediction. These point prediction approaches do not include
the probabilistic nature of the failure. The few probabilistic approaches to
date either include the aleatoric uncertainty (which originates from the
system), or the epistemic uncertainty (which originates from the model
parameters), or both simultaneously as a total uncertainty. Here, we propose
ensemble neural networks for probabilistic RUL predictions which considers both
uncertainties and decouples these two uncertainties. These decoupled
uncertainties are vital in knowing and interpreting the confidence of the
predictions. This method is tested on NASA's turbofan jet engine CMAPSS
data-set. Our results show how these uncertainties can be modeled and how to
disentangle the contribution of aleatoric and epistemic uncertainty.
Additionally, our approach is evaluated on different metrics and compared
against the current state-of-the-art methods.

使用集成神经网络进行概率剩余寿命预测的方法，可以区分出系统产生的 aleatoric 不确定性和模型参数产生的 (epistemic) 不确定性，并通过在 NASA 的涡轮喷气发动机 CMAPSS 数据集上的测试，展示了这些不确定性可以被建模和解释的方式，并与现有的先进方法进行了评估。

用于剩余使用寿命预测的集成神经网络

Ensemble Neural Networks for Remaining Useful Life (RUL) Prediction

With changing climatic conditions, we are already seeing an increase in
extreme weather events and their secondary consequences, including landslides.
Landslides threaten infrastructure, including roads, railways, buildings, and
human life. Hazard-based spatial planning and early warning systems are
cost-effective strategies to reduce the risk to society from landslides.
However, these both rely on data from previous landslide events, which is often
scarce. Many deep learning (DL) models have recently been applied for landside
mapping using medium- to high-resolution satellite images as input. However,
they often suffer from sensitivity problems, overfitting, and low mapping
accuracy. This study addresses some of these limitations by using a diverse
global landslide dataset, using different segmentation models, such as Unet,
Linknet, PSP-Net, PAN, and DeepLab and based on their performances, building an
ensemble model. The ensemble model achieved the highest F1-score (0.69) when
combining both Sentinel-1 and Sentinel-2 bands, with the highest average
improvement of 6.87 % when the ensemble size was 20. On the other hand,
Sentinel-2 bands only performed very well, with an F1 score of 0.61 when the
ensemble size is 20 with an improvement of 14.59 % when the ensemble size is
20. This result shows considerable potential in building a robust and reliable
monitoring system based on changes in vegetation index dNDVI only.

使用多个分割模型和不同的数据集，构建一个集成模型来提高山体滑坡地貌图的制作精度。集成模型使用 Sentinel-1 和 Sentinel-2 波段结合，以及通过植被指数 dNDVI 的变化来构建一个健全可靠的监测系统。