2025年1月8日 · Spatial Stream Network (SSN) models are unique because they account for patterns of spatial autocorrelation among locations based on both Euclidean and in-stream distances. They also have practical applications for the design of monitoring strategies and the derivation of information from databases with non-random sample locations.

Fit SSN models in R using “SSN” package and generate predictions for status maps.

Spatial stream network (SSN) models use a spatial statistical modeling framework to describe unique and complex dependencies on a stream network resulting from a branching network structure, directional water flow, and differences in flow volume.

2023年1月30日 · 本文介绍了一种名为测试流扫描网络(ssn)的技术，该技术能够解决大型片上系统(soc)设计中测试设计(dft)的复杂性问题。ssn不仅简化了dft的规划和实现工作，还优化了测试数据的时间和体量，同时支持各种atpg模式类型和故障模型。

We also determined whether a spatial stream network (SSN) modeling approach improved on multiple linear regression (nonspatial) models. Goodness of fit and predic-tive accuracy were highest for the SSN model that included local covariates, and lowest for the nonspatial model developed from national data.

2024年10月16日 · The SSN models outperform traditional statistical techniques applied to stream data, enable predictions at unsampled locations to create status maps for river networks, and work particularly well with databases aggregated from multiple sources that contain clustered sampling locations.

It includes implementation for SSN as well as other STOA frameworks for various tasks (action classification, temporal action detection, and spatial-temporal action detection). The lessons we learned in this repo are incorporated into MMAction to make it bettter.

Plot fitted model diagnostics such as residuals vs fitted values, quantile-quantile, scale-location, Cook's distance, residuals vs leverage, and Cook's distance vs leverage.

Spatial stream network (SSN) models use a spatial statistical modeling framework to describe unique and complex dependencies on a stream network resulting from a branching network structure, directional water flow, and differences in flow volume.

SSN实现流程基于用于分层设计的Tessent Shell流，并利用Tessent Connect自动化。专用的调试和验证功能--包括DRCS、测试台、SSN连续性模式、SSN回环模式和IJTAG生存模式上的流--确保了一个平稳有效的DFT实现流程。