LiDAR is currently one of the most utilized sensors to effectively monitor
the status of power lines and facilitate the inspection of remote power
distribution networks and related infrastructures. To ensure the safe operation
of the smart grid, various remote data acquisition strategies, such as Airborne
Laser Scanning (ALS), Mobile Laser Scanning (MLS), and Terrestrial Laser
Scanning (TSL) have been leveraged to allow continuous monitoring of regional
power networks, which are typically surrounded by dense vegetation. In this
article, an unsupervised Machine Learning (ML) framework is proposed, to
detect, extract and analyze the characteristics of power lines of both high and
low voltage, as well as the surrounding vegetation in a Power Line Corridor
(PLC) solely from LiDAR data. Initially, the proposed approach eliminates the
ground points from higher elevation points based on statistical analysis that
applies density criteria and histogram thresholding. After denoising and
transforming of the remaining candidate points by applying Principle Component
Analysis (PCA) and Kd-tree, power line segmentation is achieved by utilizing a
two-stage DBSCAN clustering to identify each power line individually. Finally,
all high elevation points in the PLC are identified based on their distance to
the newly segmented power lines. Conducted experiments illustrate that the
proposed framework is an agnostic method that can efficiently detect the power
lines and perform PLC-based hazard analysis.

利用无人监督机器学习框架，从 LiDAR 数据中检测、提取和分析高低压电力线路以及周围植被的特征，进而进行基于电力线路走廊的危险性分析。

智能电网中用于电力线路检测的自主点云分割

Autonomous Point Cloud Segmentation for Power Lines Inspection in Smart  Grid

Large Language Models (LLMs), such as GPT-3, have demonstrated remarkable
natural language processing and generation capabilities and have been applied
to a variety tasks, such as source code generation. This paper explores the
potential of integrating LLMs in the hazard analysis for safety-critical
systems, a process which we refer to as co-hazard analysis (CoHA). In CoHA, a
human analyst interacts with an LLM via a context-aware chat session and uses
the responses to support elicitation of possible hazard causes. In this
experiment, we explore CoHA with three increasingly complex versions of a
simple system, using Open AI's ChatGPT service. The quality of ChatGPT's
responses were systematically assessed to determine the feasibility of CoHA
given the current state of LLM technology. The results suggest that LLMs may be
useful for supporting human analysts performing hazard analysis.

本文探讨了将大型语言模型与人类分析师结合，应用于安全关键系统的危险分析中，通过与 OpenAI 的 ChatGPT 服务交互，评估其可行性，结果表明 LLMs 可以支持人类分析师执行危险分析。

大型语言模型是否可以辅助危险分析？

Can Large Language Models assist in Hazard Analysis?

Codex, a large language model (LLM) trained on a variety of codebases,
exceeds the previous state of the art in its capacity to synthesize and
generate code. Although Codex provides a plethora of benefits, models that may
generate code on such scale have significant limitations, alignment problems,
the potential to be misused, and the possibility to increase the rate of
progress in technical fields that may themselves have destabilizing impacts or
have misuse potential. Yet such safety impacts are not yet known or remain to
be explored. In this paper, we outline a hazard analysis framework constructed
at OpenAI to uncover hazards or safety risks that the deployment of models like
Codex may impose technically, socially, politically, and economically. The
analysis is informed by a novel evaluation framework that determines the
capacity of advanced code generation techniques against the complexity and
expressivity of specification prompts, and their capability to understand and
execute them relative to human ability.

研究提出了一个基于规范提示的先进代码生成技术能力评估框架，并构建了 OpenAI 的危害分析框架，以揭示类似 Codex 模型的部署可能会对技术、社会、政治和经济方面带来的危险或安全风险。