面向光学遥感卫星星上定位精度优化的轻量化矢量控制库技术 DOI Open Access

李明 Li Ming,

董杨 Dong Yang,

范大昭 Fan Dazhao

et al.

Acta Optica Sinica, Journal Year: 2024, Volume and Issue: 44(6), P. 0628003 - 0628003

Published: Jan. 1, 2024

针对当前光学智能遥感卫星有限存储能力对全球控制信息的轻量化需求,提出一种面向光学遥感卫星星上定位精度优化的轻量化矢量控制库技术。首先,在地面提取完整道路网,通过道路细化、节点提取以及拓扑关系构建等处理,生成星上轻量化矢量控制库并上注卫星;其次,星上在轨提取道路结构,并利用随机游走避免道路缺失的影响,生成随机游走矢量结构;然后,引入隐马尔科夫模型,搜索对应矢量,并设计分层匹配策略以精化匹配结果,实现星上轻量化矢量控制库与随机游走矢量结构的匹配;最后,利用不同类型卫星影像进行随机游走矢量结构提取、星上矢量匹配以及定位性能分析。结果表明,所提光学遥感卫星的星上轻量化矢量控制库能够有效改善非量测光学遥感卫星定位精度,验证了其在光学智能遥感卫星中的可行性。

Anomaly Detection in Smart Environments: A Comprehensive Survey DOI Creative Commons
Daniel Fährmann, Laura Martín, Luı́s Sánchez

et al.

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 64006 - 64049

Published: Jan. 1, 2024

Anomaly detection is a critical task in ensuring the security and safety of infrastructure individuals smart environments.This paper provides comprehensive analysis recent anomaly solutions data streams supporting environments, with specific focus on multivariate time series various such as home, transport, industry.The aim to offer thorough overview current state-of-the-art techniques applicable these includes an examination publicly available datasets suitable for developing techniques.The survey designed inform future research practical applications field, serving valuable resource researchers practitioners.It not only reviews range methods, from statistical proximity-based those adopting deep learning-methods but also covers fundamental aspects detection.These include categorization anomalies, scenarios, challenges associated, evaluation metrics assessing techniques' performance.

Language: Английский

Citations

7
A simple rapid sample-based clustering for large-scale data DOI
Yewang Chen, Yuanyuan Yang, Songwen Pei

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2024, Volume and Issue: 133, P. 108551 - 108551

Published: May 11, 2024

Language: Английский

Citations

4
Escape velocity-based adaptive outlier detection algorithm DOI

Jinchuan Yang,

Lijun Yang, Dongming Tang

et al.

Knowledge-Based Systems, Journal Year: 2025, Volume and Issue: 311, P. 113116 - 113116

Published: Feb. 1, 2025

Language: Английский

Citations

0
Rectifying inaccurate unsupervised learning for robust time series anomaly detection DOI
Zejian Chen,

Zuoyong Li,

Xinwei Chen

et al.

Information Sciences, Journal Year: 2024, Volume and Issue: 662, P. 120222 - 120222

Published: Jan. 29, 2024

Language: Английский

Citations

3
Edge conditional node update graph neural network for multivariate time series anomaly detection DOI
Hayoung Jo, Seong‐Whan Lee

Information Sciences, Journal Year: 2024, Volume and Issue: 679, P. 121062 - 121062

Published: June 21, 2024

Language: Английский

Citations

2
Adaptive gravitational clustering algorithm integrated with noise detection DOI
Juntao Yang, Lijun Yang, Wentong Wang

et al.

Expert Systems with Applications, Journal Year: 2024, Volume and Issue: unknown, P. 125733 - 125733

Published: Nov. 1, 2024

Language: Английский

Citations

2
Machine learning models with innovative outlier detection techniques for predicting heavy metal contamination in soils DOI
Ram Proshad, S Asha,

Rong Kun Jason Tan

et al.

Journal of Hazardous Materials, Journal Year: 2024, Volume and Issue: 481, P. 136536 - 136536

Published: Nov. 19, 2024

Language: Английский

Citations

2
Deep Anomaly Detection: A Linear One-Class SVM Approach for High-Dimensional and Large-Scale Data DOI

K. Suresh,

K. Jayasakthi Velmurugan,

R.G. Vidhya

et al.

Applied Soft Computing, Journal Year: 2024, Volume and Issue: 167, P. 112369 - 112369

Published: Oct. 24, 2024

Language: Английский

Citations

1
面向光学遥感卫星星上定位精度优化的轻量化矢量控制库技术 DOI Open Access

李明 Li Ming,

董杨 Dong Yang,

范大昭 Fan Dazhao

et al.

Acta Optica Sinica, Journal Year: 2024, Volume and Issue: 44(6), P. 0628003 - 0628003

Published: Jan. 1, 2024

针对当前光学智能遥感卫星有限存储能力对全球控制信息的轻量化需求,提出一种面向光学遥感卫星星上定位精度优化的轻量化矢量控制库技术。首先,在地面提取完整道路网,通过道路细化、节点提取以及拓扑关系构建等处理,生成星上轻量化矢量控制库并上注卫星;其次,星上在轨提取道路结构,并利用随机游走避免道路缺失的影响,生成随机游走矢量结构;然后,引入隐马尔科夫模型,搜索对应矢量,并设计分层匹配策略以精化匹配结果,实现星上轻量化矢量控制库与随机游走矢量结构的匹配;最后,利用不同类型卫星影像进行随机游走矢量结构提取、星上矢量匹配以及定位性能分析。结果表明,所提光学遥感卫星的星上轻量化矢量控制库能够有效改善非量测光学遥感卫星定位精度,验证了其在光学智能遥感卫星中的可行性。

Citations

0