scholarly journals Prediction and control of behaviors on driving shields using Kalman filter theory.

1987 ◽  
pp. 69-78 ◽  
Author(s):  
Kunito SAKAI ◽  
Masaru HOSHIYA
Keyword(s):  
Kalman Filter ◽  
Filter Theory ◽  
Prediction And Control ◽  
And Control

scholarly journals COVID-19: Modeling, Prediction, and Control

2020 ◽  
Vol 10 (11) ◽  
pp. 3666 ◽  
Author(s):  
Ahmad Bani Younes ◽  
Zeaid Hasan
Keyword(s):  
Kalman Filter ◽  
Control Measures ◽  
World Health ◽  
Volterra Model ◽  
The World ◽  
Prediction And Control ◽  
Modeling Prediction ◽  
Prime Objective ◽  
Health Organization ◽  
And Control

The newly emerging severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), also known as COVID-19, has been recognized as a pandemic by the World Health Organization (WHO) on 11th March 2020. There are many unknowns about this virus to date and no vaccine or conclusive treatment due to the lack of understanding of its pathogenesis and proliferation pathways which are unknown and cannot be traced. The prime objective is to stop its spread worldwide. This article aims to provide predictions of its spread using a stochastic Lotka–Volterra model coupled with an extended Kalman Filter (EKF) algorithm to model the COVID-19 dynamics. Our results show the feasibility of utilizing this model for predicting the spread of the virus and the ability of different control measures (e.g., social distancing) on reducing the number of affected people.

An insight into Case Management and Challenges in Veteran Suicide Prevention

2020 ◽  
pp. 14-25
Author(s):  
Tamara Green
Keyword(s):  
Case Management ◽  
Suicide Prevention ◽  
Suicide Risk ◽  
Digital Transformation ◽  
Prevention Science ◽  
Historical Records ◽  
Military Members ◽  
Prediction And Control ◽  
And Control ◽  
Insight Into

Much of the literature, policies, programs, and investment has been made on mental health, case management, and suicide prevention of veterans. The Australian “veteran community is facing a suicide epidemic for the reasons that are extremely complex and beyond the scope of those currently dealing with them.” (Menz, D: 2019). Only limited work has considered the digital transformation of loosely and manual-based historical records and no enablement of Artificial Intelligence (A.I) and machine learning to suicide risk prediction and control for serving military members and veterans to date. This paper presents issues and challenges in suicide prevention and management of veterans, from the standing of policymakers to stakeholders, campaigners of veteran suicide prevention, science and big data, and an opportunity for the digital transformation of case management.

A case study of wear-type rail corrugation prediction and control using speed variation

2009 ◽  
Vol 325 (1-2) ◽  
pp. 85-105 ◽  
Author(s):  
P.A. Meehan ◽  
P.A. Bellette ◽  
R.D. Batten ◽  
W.J.T. Daniel ◽  
R.J. Horwood
Keyword(s):  
Rail Corrugation ◽  
Speed Variation ◽  
Prediction And Control ◽  
And Control

Phenomenological Foundations for the Study of Suicide

1973 ◽  
Vol 4 (3) ◽  
pp. 195-208
Author(s):  
Keith Hoeller
Keyword(s):  
Great Emphasis ◽  
Prediction And Control ◽  
And Control

Is death the “enemy” to be avoided at all costs or is it to be faced, engendering liberation and rebirth? Contemporary suicidology concerns itself with the “causes” of suicide, placing great emphasis on prediction and control However, when the “meaning” of suicide is studied, understanding it as a human phenomenon becomes of major concern. Part of this understanding requires one to view “dread” as implying the possibility of making one's existence one's own, rather than something that must be prevented. In the study of suicide, revolutionary insights can emerge if less emphasis is placed on death as the “enemy” and more attention is placed on “dread” as a potential liberator.

scholarly journals Subgraphs of Interest Social Networks for Diffusion Dynamics Prediction

Entropy ◽  
2021 ◽  
Vol 23 (4) ◽  
pp. 492
Author(s):  
Valentina Y. Guleva ◽  
Polina O. Andreeva ◽  
Danila A. Vaganov
Keyword(s):  
Social Networks ◽  
Sampling Method ◽  
Building Blocks ◽  
Global Dynamics ◽  
Diffusion Dynamics ◽  
Different Types ◽  
Prediction And Control ◽  
Path Lengths ◽  
And Control ◽  
Diffusion Prediction

Finding the building blocks of real-world networks contributes to the understanding of their formation process and related dynamical processes, which is related to prediction and control tasks. We explore different types of social networks, demonstrating high structural variability, and aim to extract and see their minimal building blocks, which are able to reproduce supergraph structural and dynamical properties, so as to be appropriate for diffusion prediction for the whole graph on the base of its small subgraph. For this purpose, we determine topological and functional formal criteria and explore sampling techniques. Using the method that provides the best correspondence to both criteria, we explore the building blocks of interest networks. The best sampling method allows one to extract subgraphs of optimal 30 nodes, which reproduce path lengths, clustering, and degree particularities of an initial graph. The extracted subgraphs are different for the considered interest networks, and provide interesting material for the global dynamics exploration on the mesoscale base.

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