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

While end-to-end learning systems are rapidly gaining capabilities and
popularity, the increasing computational demands for deploying such systems,
along with a lack of flexibility, adaptability, explainability, reasoning and
verification capabilities, require new types of architectures. Here we
introduce a classification of hybrid systems which, based on an analysis of
human knowledge and intelligence, combines neural learning with various types
of knowledge and knowledge sources. We present the Thrill-K architecture as a
prototypical solution for integrating instantaneous knowledge, standby
knowledge and external knowledge sources in a framework capable of inference,
learning and intelligent control.

本研究介绍了一种基于人类知识和智能分析的混合系统分类，并提出了 Thrill-K 框架作为一种原型解决方案，该框架能够集成瞬时知识、待机知识和外部知识来源，在推理、学习和智能控制方面发挥作用。

Thrill-K 架构：解决基于知识理解的问题的解决方案

Thrill-K Architecture: Towards a Solution to the Problem of Knowledge Based Understanding

To keep supporting next-generation requirements, the radio access
infrastructure will increasingly densify. Cell-free (CF) network architectures
are emerging, combining dense deployments with extreme flexibility in
allocating resources to users. In parallel, the Open Radio Access Networks
(O-RAN) paradigm is transforming RAN towards an open, intelligent, virtualized,
and fully interoperable architecture. This paradigm brings the needed
flexibility and intelligent control opportunities for CF networking. In this
paper, we document the current O-RAN terminology and contrast it with some
common CF processing approaches. We then discuss the main O-RAN innovations and
research challenges that remain to be solved.

介绍 O-RAN 模式及其在 cell-free 网络中的应用，探讨了 O-RAN 创新和研究挑战。