State Estimation in Batch Process Based on Two-Dimensional State-Space Model

2014 ◽  
Vol 53 (50) ◽  
pp. 19573-19582 ◽  
Author(s):  
Zhonggai Zhao ◽  
Biao Huang ◽  
Fei Liu
Keyword(s):  
State Estimation ◽  
State Space ◽  
State Space Model ◽  
Batch Process ◽  
Two Dimensional ◽  
Space Model ◽  
Dimensional State Space

scholarly journals Implicit–explicit gradient of nondual awareness or consciousness as such

2021 ◽  
Vol 2021 (2) ◽  
Author(s):  
Zoran Josipovic
Keyword(s):  
State Space ◽  
State Space Model ◽  
Neural Correlates ◽  
Scientific Understanding ◽  
The Other ◽  
Two Dimensional ◽  
Global State ◽  
Phenomenal Content ◽  
Space Model ◽  
Dimensional State Space

Abstract Consciousness is multi-dimensional but is most often portrayed with a two-dimensional (2D) map that has global levels or states on one axis and phenomenal contents on the other. On this map, awareness is conflated either with general alertness or with phenomenal content. This contributes to ongoing difficulties in the scientific understanding of consciousness. Previously, I have proposed that consciousness as such or nondual awareness—a basic non-conceptual, non-propositional awareness in itself free of subject-object fragmentation—is a unique kind that cannot be adequately specified by this 2D map of states and contents. Here, I propose an implicit–explicit gradient of nondual awareness to be added as the z-axis to the existing 2D map of consciousness. This gradient informs about the degree to which nondual awareness is manifest in any experience, independent of the specifics of global state or local content. Alternatively, within the multi-dimensional state space model of consciousness, nondual awareness can be specified by several vectors, each representing one of its properties. In the first part, I outline nondual awareness or consciousness as such in terms of its phenomenal description, its function and its neural correlates. In the second part, I explore the implicit–explicit gradient of nondual awareness and how including it as an additional axis clarifies certain features of everyday dualistic experiences and is especially relevant for understanding the unitary and nondual experiences accessed via different contemplative methods, mind-altering substances or spontaneously.

State-Space Model for Traffic State Estimation of a Two-Dimensional Network

2018 ◽  
Vol 13 (2) ◽  
pp. 326-337
Author(s):  
Yosuke Kawasaki ◽  
Yusuke Hara ◽  
Masao Kuwahara ◽  
◽  
◽  
...  
Keyword(s):  
State Space ◽  
Real Time ◽  
Traffic Flow ◽  
Flow Model ◽  
State Space Model ◽  
Two Dimensional ◽  
Traffic Flow Model ◽  
Space Model ◽  
Traffic State ◽  
2D Network

This study proposes a real-time monitoring method for two-dimensional (2D) networks via the fusion of probe data and a traffic flow model. In the Great East Japan Earthquake occurring on March 11, 2011, there was major traffic congestion as evacuees concentrated in cities on the Sanriku Coast. A tragedy occurred when a tsunami overtook the stuck vehicles. To evacuate safely and efficiently, the state of traffic must be monitored in real time on a 2D network, where all networks are linked. Generally, the traffic state is monitored only at observation points. However, observation data presents the risk of errors. Additionally, in the estimated traffic state of the 2D network, unlike non-intersecting road sections (i.e., one-dimensional), it is necessary to model user route choice behavior and origin/destination (OD) demand to input in the model. Therefore, in this study, we develop a state-space model that assimilates vehicle density and divergence ratio data obtained from probe vehicles in a traffic flow model that considers route choice. Our state-space model considers observational errors in the probe data and can simultaneously estimate traffic state and destination component ratio of OD demand. The result of simulated traffic model verification shows that the proposed model has good congestion estimation precision in a small-scale test network.

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