A real-time battle situation intelligent awareness system based on Meta-learning & RNN
원문 초록
In modern warfare, real-time and accurate battle situation analysis is crucial for making strategic and tactical decisions. The proposed real-time battle situation intelligent awareness system (BSIAS) aims at meta-learning analysis and stepwise RNN (recurrent neural network) modeling, where the former carries out the basic processing and analysis of battlefield data, which includes multi-steps such as data cleansing, data fusion, data mining and continuously updates, and the latter optimizes the battlefield modeling by stepwise capturing the temporal dependencies of data set. BSIAS can predict the possible movement from any side of the fence and attack routes by taking a simulated battle as an example, which can be an intelligent support platform for commanders to make scientific decisions during wartime. This work delivers the potential application of integrated BSIAS in the field of battlefield command & analysis engineering.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Geo-ORBIT: A Federated Digital Twin Framework for Scene-Adaptive Lane Geometry Detection
DIP: Unsupervised Dense In-Context Post-training of Visual Representations
Episode-specific Fine-tuning for Metric-based Few-shot Learners with Optimization-based Training
Meta-learning how to Share Credit among Macro-Actions
Latent Representation Learning of Multi-scale Thermophysics: Application to Dynamics in Shocked Porous Energetic Material
Robust LLM Unlearning with MUDMAN: Meta-Unlearning with Disruption Masking And Normalization
토론
이 논문과 연결된 포럼 글이 아직 없습니다.