Systems of intelligent control of manual operations in industrial production
are being implemented in many industries nowadays. Such systems use
high-resolution cameras and computer vision algorithms to automatically track
the operator's manipulations and prevent technological errors in the assembly
process. At the same time compliance with safety regulations in the workspace
is monitored. As a result, the defect rate of manufactured products and the
number of accidents during the manual assembly of any device are decreased.
Before implementing an intelligent control system into a real production it is
necessary to calculate its efficiency. In order to do it experiments on the
stand for manual operations control systems were carried out. This paper
proposes the methodology for calculating the efficiency indicators. This
mathematical approach is based on the IoU calculation of real- and
predicted-time intervals between assembly stages. The results show high
precision in tracking the validity of manual assembly and do not depend on the
duration of the assembly process.

工业生产中的机器操作智能控制系统正在许多行业中得到实施。该系统使用高分辨率摄像机和计算机视觉算法，自动跟踪操作者的操纵，并防止组装过程中的技术错误。同时，它还监控工作区域的安全规定。结果导致产品的缺陷率和任何设备的手动组装过程中的事故数量减少。在将智能控制系统引入实际生产之前，有必要计算其效率。为此，在手动操作控制系统方面的实验已经进行了。本文提出了计算效率指标的方法论。这种数学方法基于装配阶段之间实际时间和预计时间间隔的 IoU 计算。结果显示，在跟踪手动组装的有效性方面具有高精度，并且不受组装过程时间的影响。

智能控制工业生产人工操作台的效率指标确定

Determination of efficiency indicators of the stand for intelligent  control of manual operations in industrial production

The new industrial settings are characterized by the presence of human and
robots that work in close proximity, cooperating in performing the required
job. Such a collaboration, however, requires to pay attention to many aspects.
Firstly, it is crucial to enable a communication between this two actors that
is natural and efficient. Secondly, the robot behavior must always be compliant
with the safety regulations, ensuring always a safe collaboration. In this
paper, we propose a framework that enables multi-channel communication between
humans and robots by leveraging multimodal fusion of voice and gesture commands
while always respecting safety regulations. The framework is validated through
a comparative experiment, demonstrating that, thanks to multimodal
communication, the robot can extract valuable information for performing the
required task and additionally, with the safety layer, the robot can scale its
speed to ensure the operator's safety.

本文提出了一个多通道通信框架，通过结合音频和手势命令的多模态融合，实现了人与机器人之间的自然高效沟通，并且始终遵守安全规定。通过比较实验验证了该框架的有效性，证明了多模态沟通使得机器人能够提取有价值的信息并且通过安全层适应速度以确保操作人员的安全。

人机协作中的安全多模态通信

Safe Multimodal Communication in Human-Robot Collaboration

New emerging technologies powered by Artificial Intelligence (AI) have the
potential to disruptively transform our societies for the better. In
particular, data-driven learning approaches (i.e., Machine Learning (ML)) have
been a true revolution in the advancement of multiple technologies in various
application domains. But at the same time there is growing concern about
certain intrinsic characteristics of these methodologies that carry potential
risks to both safety and fundamental rights. Although there are mechanisms in
the adoption process to minimize these risks (e.g., safety regulations), these
do not exclude the possibility of harm occurring, and if this happens, victims
should be able to seek compensation. Liability regimes will therefore play a
key role in ensuring basic protection for victims using or interacting with
these systems. However, the same characteristics that make AI systems
inherently risky, such as lack of causality, opacity, unpredictability or their
self and continuous learning capabilities, may lead to considerable
difficulties when it comes to proving causation. This paper presents three case
studies, as well as the methodology to reach them, that illustrate these
difficulties. Specifically, we address the cases of cleaning robots, delivery
drones and robots in education. The outcome of the proposed analysis suggests
the need to revise liability regimes to alleviate the burden of proof on
victims in cases involving AI technologies.

阐述了人工智能和机器学习技术在改善社会的同时，由于潜在的风险和固有的特征会导致安全和基本权利受到威胁，并提出需要改进现有的责任制度以保护受害者的利益的观点。