用量子隧穿机制提升军民车辆分类与战场情感分析的准确性
Quantum-Cognitive Tunnelling Neural Networks for Military-Civilian Vehicle Classification and Sentiment Analysis
- 引入量子隧穿概率建模神经网络，模拟人类感知模糊性
- 在定制版CIFAR图像上实现军民车辆精准区分，效果优于传统模型
- 适合军事场景下的多模态智能系统开发，尤其适用于无人机作战
先前研究已证明，将经典的量子隧穿（QT）概率引入神经网络模型，能有效捕捉人类感知中的细微差异，尤其在识别模糊物体和情感分析方面表现突出。本文采用新型基于量子隧穿的神经网络，评估其在区分定制化CIFAR格式军用与民用车辆图像，以及使用专有军事词汇进行情感分析方面的有效性。结果表明，基于量子隧穿的模型可增强战场环境下多模态AI应用能力，尤其在人机协同无人机作战中，赋予AI部分类人推理特性。
原文摘要 · Abstract (English)
Prior work has demonstrated that incorporating well-known quantum tunnelling (QT) probability into neural network models effectively captures important nuances of human perception, particularly in the recognition of ambiguous objects and sentiment analysis. In this paper, we employ novel QT-based neural networks and assess their effectiveness in distinguishing customised CIFAR-format images of military and civilian vehicles, as well as sentiment, using a proprietary military-specific vocabulary. We suggest that QT-based models can enhance multimodal AI applications in battlefield scenarios, particularly within human-operated drone warfare contexts, imbuing AI with certain traits of human reasoning.
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