Rock characterization while drilling and application of roof bolter drilling data for evaluation of ground conditions

Despite recent advances in mine health and safety, roof collapse and instabilities are still the leading causes of injury and fatality in underground mining operations. Improving safety and optimum design of ground support requires good and reliable ground characterization. While many geophysical methods have been developed for ground characterizations, their accuracy is insufficient for customized ground support design of underground workings. The actual measurements on the samples of the roof and wall strata from the exploration boring are reliable but the related holes are far apart, thus unsuitable for design purposes. The best source of information could be the geological back mapping of the roof and walls, but this is disruptive to mining operations, and provided information is only from rock surface.Interpretation of the data obtained from roof bolt drilling can offer a good and reliable source of information that can be used for ground characterization and ground support design and evaluations. This paper offers a brief review of the mine roof characterization methods, followed by introduction and discussion of the roof characterization methods by instrumented roof bolters. A brief overview of the results of the preliminary study and initial testing on an instrumented drill and summary of the suggested improvements are also discussed.

GPT-4 enhanced multimodal grounding for autonomous driving:Leveraging cross-modal attention with large language models

In the field of autonomous vehicles(AVs),accurately discerning commander intent and executing linguistic commands within a visual context presents a significant challenge.This paper introduces a sophisticated encoder-decoder framework,developed to address visual grounding in AVs.Our Context-Aware Visual Grounding(CAVG)model is an advanced system that integrates five core encoders—Text,Emotion,Image,Context,and Cross-Modal—with a multimodal decoder.This integration enables the CAVG model to adeptly capture contextual semantics and to learn human emotional features,augmented by state-of-the-art Large Language Models(LLMs)including GPT-4.The architecture of CAVG is reinforced by the implementation of multi-head cross-modal attention mechanisms and a Region-Specific Dynamic(RSD)layer for attention modulation.This architectural design enables the model to efficiently process and interpret a range of cross-modal inputs,yielding a comprehensive understanding of the correlation between verbal commands and corresponding visual scenes.Empirical evaluations on the Talk2Car dataset,a real-world benchmark,demonstrate that CAVG establishes new standards in prediction accuracy and operational efficiency.Notably,the model exhibits exceptional performance even with limited training data,ranging from 50%to 75%of the full dataset.This feature highlights its effectiveness and potential for deployment in practical AV applications.Moreover,CAVG has shown remarkable robustness and adaptability in challenging scenarios,including long-text command interpretation,low-light conditions,ambiguous command contexts,inclement weather conditions,and densely populated urban environments.