industrial safety engineers

Abstract: Incorporating AI systems into safety systems requires monitoring to avoid silent faults and ensure safe and reliable operation. Single monitors often do not provide actionable information for human supervisors, who act as the system's adaptive controller. We present COMET, a coordinated multi-layer runtime monitoring framework for human-supervised AI-enabled safety systems in which each monitor is anchored in a different fault mode. Monitor outputs are combined in a coordination layer and mapped by a communication layer to a traffic-light display. In this work, COMET consists of three monitoring layers: signal integrity, physics consistency, and the AI system's competence envelope. The framework is evaluated on an open dataset of compressed-air leaks, where an AI system detects leaks from acoustic emissions, with held-out conditions simulating covariate shift, semantic shift, and sensor faults, events expected during runtime but unknown in their exact appearance. All monitors are frozen after development and tested zero-shot, and the framework is compared against no-monitoring and single-monitor setups. The bare AI system fails silently and confidently under all three shift types, whereas COMET reduces the trusted-wrong-answer rate to zero on covariate-shifted no-leak data and on three of four sensor faults, at a nominal false-alarm rate of $\approx 1\%$. Single monitors come close in detection but cannot attribute the fault mode. COMET converts their anonymous detection flags into diagnoses. Residual silent misses under semantic shift define the boundary of passive monitoring. Runtime monitoring frameworks for AI-enabled safety systems must therefore combine quantitative fault detection and isolation with qualitative, human-oriented feedback.

Abstract: Infrared small target detection is often used in different civilian and military applications to detect static or moving targets in scenes like in ground, air and sea surveillance. First valuable research has emerged since 30 years ago and the two last decades witnessed very significant publications in this field following the rise of low-rank and sparse decomposition (model-based methods) and deep learning models (data-based methods). Furthermore, several surveys can be found in the literature but none of them addresses an overall review in this field. In this context, the aim of this paper is to provide a complete survey of the traditional and recent approaches in infrared small target detection. First, we categorize the different approaches found in the literature in terms of the modeling models, i.e mathematical models, machine learning models and signal processing models. Then, we have discussed them in terms of the critical situations that they claim to handle. Furthermore, we present the available resources, datasets and libraries. Then, we conclude with several promising directions for future research. For the sake of reading, this survey is divided into two parts: 1) "Traditional and Recent Approaches for Infrared Small Target Detection : Overview, Challenges and Perspectives (Part 1)" about conventional and low-rank and sparse decomposition (model-based methods), and 2) "Traditional and Recent Approaches for Infrared Small Target Detection : Overview, Challenges and Perspectives (Part 2)" about deep learning models (data-based methods).

Abstract: There are renewed efforts to build a lunar base and house a team of astronauts on the Moon for extended periods. However, important challenges remain, such as a rapid-response fire-hazard management system. In the event of a fire or toxic leak inside an early crewed lunar habitat, a single pressurized cylinder must be handled within minutes. However, suited astronauts and radio-delayed support from Earth cannot guarantee such response times. We describe a prototype fire-fighting and emergency response safety technology that embeds intelligence in the habitat itself: using thin ceramic QR codes affixed every few meters, each carries a local floor map, safe sensor limits, and step-by-step hazard responses. A low-power rover decodes a plate in a matter of seconds, polls the adjoining temperature-gas sensor cluster, and acts immediately, eliminating the need for a global map . In a representative layout, the interior is divided into roughly one code per two square meters. Simulations show that rapid-response firefighting both simplifies the task and avoids costly resource use and indeterminate outcomes. Because knowledge is spread across passive plates, damage is localized, procedures can be updated by replacing a single code, and processor demands remain minimal. However, important challenges remain, such as a rapid-response fire-hazard management system. In the event of a fire or toxic leak inside an early crewed lunar habitat, a single pressurized cylinder must be handled within minutes. However, suited astronauts and radio-delayed support from Earth cannot guarantee such response times. We describe a prototype fire-fighting and emergency response safety technology that embeds intelligence in the habitat itself: using thin ceramic QR codes affixed every few meters, each carries a local floor map, safe sensor limits, and step-by-step hazard responses.

Abstract: A robot sent to a named gas leak must preserve gas identity, estimate the source, and navigate to the resulting goal. We present SmellDiffusion, a simulation pipeline that represents species-specific gas zones in an open-vocabulary olfactory scene graph and shares the selected goal between classical and diffusion planners. Its key components are a peak-local geometric gate for selective source correction and diffusion-based, gas-guided trajectory generation. Among 424 unique source-wind configurations in solved flow, 28 have a concentration peak displaced more than 0.5m from the source. A source-independent geometric gate, calibrated only on the training split and evaluated at the observed peak, detects 9 of 10 held-out displacements at 0.64 precision. Gating a precomputed forward-matching correction reduces mean error on the displaced cases from 1.468m to 0.592m (60%), using matching for only 14/204 cases. All-case mean error falls from 0.205m to 0.180m. All planners receive the same scene-graph source estimate as their goal. In a controlled comparison, best-of-ten diffusion achieves mean gas exposure comparable to gas-guided A* (0.0476 versus 0.0455). A single diffusion proposal takes 41.7ms, compared with 72.3ms for gas-guided A*, although best-of-ten sequential sampling increases total runtime. Plain A* also reaches the same goal and remains the fastest and shortest-path method. Six matched Gazebo runs give mean robot-to-source errors of 0.39m for A* and 0.31m for diffusion.