Nonlinear climate dynamics: From deterministic behavior to stochastic excitability and chaos

2020 ◽  
Author(s):  
Dmitri V. Alexandrov ◽  
Irina A. Bashkirtseva ◽  
Lev B. Ryashko ◽  
Michel Crucifix
Keyword(s):  
Climate Dynamics ◽  
Deterministic Behavior

scholarly journals On-Device Deep Learning Inference for System-on-Chip (SoC) Architectures

Electronics ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 689
Author(s):  
Tom Springer ◽  
Elia Eiroa-Lledo ◽  
Elizabeth Stevens ◽  
Erik Linstead
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Real Time ◽  
Operating Systems ◽  
System On Chip ◽  
Low Latency ◽  
Management Framework ◽  
On Chip ◽  
Specialized Hardware ◽  
Deterministic Behavior

As machine learning becomes ubiquitous, the need to deploy models on real-time, embedded systems will become increasingly critical. This is especially true for deep learning solutions, whose large models pose interesting challenges for target architectures at the “edge” that are resource-constrained. The realization of machine learning, and deep learning, is being driven by the availability of specialized hardware, such as system-on-chip solutions, which provide some alleviation of constraints. Equally important, however, are the operating systems that run on this hardware, and specifically the ability to leverage commercial real-time operating systems which, unlike general purpose operating systems such as Linux, can provide the low-latency, deterministic execution required for embedded, and potentially safety-critical, applications at the edge. Despite this, studies considering the integration of real-time operating systems, specialized hardware, and machine learning/deep learning algorithms remain limited. In particular, better mechanisms for real-time scheduling in the context of machine learning applications will prove to be critical as these technologies move to the edge. In order to address some of these challenges, we present a resource management framework designed to provide a dynamic on-device approach to the allocation and scheduling of limited resources in a real-time processing environment. These types of mechanisms are necessary to support the deterministic behavior required by the control components contained in the edge nodes. To validate the effectiveness of our approach, we applied rigorous schedulability analysis to a large set of randomly generated simulated task sets and then verified the most time critical applications, such as the control tasks which maintained low-latency deterministic behavior even during off-nominal conditions. The practicality of our scheduling framework was demonstrated by integrating it into a commercial real-time operating system (VxWorks) then running a typical deep learning image processing application to perform simple object detection. The results indicate that our proposed resource management framework can be leveraged to facilitate integration of machine learning algorithms with real-time operating systems and embedded platforms, including widely-used, industry-standard real-time operating systems.

Warm climate dynamics

GFF ◽  
2000 ◽  
Vol 122 (1) ◽  
pp. 29-30 ◽  
Author(s):  
Karen L. Bice ◽  
Jochem Marotzke
Keyword(s):  
Climate Dynamics ◽  
Warm Climate

scholarly journals Climate Dynamics of the Spatiotemporal Changes of Vegetation NDVI in Northern China from 1982 to 2015

2021 ◽  
Vol 13 (2) ◽  
pp. 187
Author(s):  
Rui Sun ◽  
Shaohui Chen ◽  
Hongbo Su
Keyword(s):  
Solar Radiation ◽  
Vegetation Index ◽  
Global Climate ◽  
Regional Scale ◽  
Northern China ◽  
Climate Dynamics ◽  
High Coverage ◽  
Vegetation Growth ◽  
Temperature And Precipitation ◽  
Precipitation And Temperature

As an important part of a terrestrial ecosystem, vegetation plays an important role in the global carbon-water cycle and energy flow. Based on the Global Inventory Monitoring and Modeling System (GIMMS) third generation of Normalized Difference Vegetation Index (NDVI3g), meteorological station data, climate reanalysis data, and land cover data, this study analyzed the climate dynamics of the spatiotemporal variations of vegetation NDVI in northern China from 1982 to 2015. The results showed that growth season NDVI (NDVIgs) increased significantly at 0.006/10a (p < 0.01) in 1982–2015 on the regional scale. The period from 1982 to 2015 was divided into three periods: the NDVIgs increased by 0.026/10a (p < 0.01) in 1982–1990, decreased by −0.002/10a (p > 0.1) in 1990–2006, and then increased by 0.021/10a (p < 0.01) during 2006–2015. On the pixel scale, the increases in NDVIgs during 1982–2015, 1982–1990, 1990–2006, and 2006–2015 accounted for 74.64%, 85.34%, 48.14%, and 68.78% of the total area, respectively. In general, the dominant climate drivers of vegetation growth had gradually switched from solar radiation, temperature, and precipitation (1982–1990) to precipitation and temperature (1990–2015). For woodland, high coverage grassland, medium coverage grassland, low coverage grassland, the dominant climate drivers had changed from temperature and solar radiation, solar radiation and precipitation, precipitation and solar radiation, solar radiation to precipitation and solar radiation, precipitation, precipitation and temperature, temperature and precipitation. The areas controlled by precipitation increased significantly, mainly distributed in arid, sub-arid, and sub-humid areas. The dominant climate drivers for vegetation growth in the plateau climate zone or high-altitude area changed from solar radiation to temperature and precipitation, and then to temperature, while in cold temperate zone, changed from temperature to solar radiation. These results are helpful to understand the climate dynamics of vegetation growth, and have important guiding significance for vegetation protection and restoration in the context of global climate change.

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