A demographic projection model to support conservation decision making for an endangered snake with limited monitoring data

2020 ◽  
Author(s):  
A. M. Tucker ◽  
C. P. McGowan ◽  
E. S. Mulero Oliveras ◽  
N. F. Angeli ◽  
J. P. Zegarra
Keyword(s):  
Decision Making ◽  
Monitoring Data ◽  
Projection Model ◽  
Demographic Projection

Multi-Zone Proportional Hazard Model for a Multi-Stage Degradation Process

2013 ◽  
Author(s):  
Lin Li ◽  
Zeyi Sun ◽  
Xinwei Xu ◽  
Kaifu Zhang
Keyword(s):  
Decision Making ◽  
Condition Monitoring ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Degradation Process ◽  
Remaining Useful Life ◽  
Monitoring Data ◽  
Status Change ◽  
Multi Stage ◽  
And Performance

Conditional-based maintenance (CBM) decision-making is of high interests in recent years due to its better performance on cost efficiency compared to other traditional policies. One of the most respected methods based on condition-monitoring data for maintenance decision-making is Proportional Hazards Model (PHM). It utilizes condition-monitoring data as covariates and identifies their effects on the lifetime of a component. Conventional modeling process of PHM only treats the degradation process as a whole lifecycle. In this paper, the PHM is advanced to describe a multi-zone degradation system considering the fact that the lifecycle of a machine can be divided into several different degradation stages. The methods to estimate reliability and performance prognostics are developed based on the proposed multi-zone PHM to predict the remaining time that the machine stays at the current stage before transferring into the next stage and the remaining useful life (RUL). The results illustrate that the multi-zone PHM effectively monitors the equipment status change and leads to a more accurate RUL prediction compared with traditional PHM.

scholarly journals Protection of Service-Oriented Environments Serving Critical Infrastructures

Inventions ◽  
2018 ◽  
Vol 3 (3) ◽  
pp. 62
Author(s):  
Dimosthenis Kyriazis
Keyword(s):  
Decision Making ◽  
High Performance ◽  
Performance Monitoring ◽  
Critical Infrastructure ◽  
Failure Detection ◽  
False Positives ◽  
Edge Computing ◽  
Monitoring Data ◽  
Critical Infrastructures ◽  
Service Oriented

The emergence of service-oriented architectures has driven the shift towards a service-oriented paradigm, which has been adopted in several application domains. The advent of cloud computing facilities and recently of edge computing environments has increased the aforementioned paradigm shift towards service provisioning. In this context, various “traditional” critical infrastructure components have turned to services, being deployed and managed on top of cloud and edge computing infrastructures. However, the latter poses a specific challenge: the services of the critical infrastructures within and across application verticals/domains (e.g., transportation, health, industrial venues, etc.) need to be continuously available with near-zero downtime. In this context, this paper presents an approach for high-performance monitoring and failure detection of critical infrastructure services that are deployed in virtualized environments. The failure detection framework consists of distributed agents (i.e., monitoring services) to ensure timely collection of monitoring data, while it is enhanced with a voting algorithm to minimize the case of false positives. The goal of the proposed approach is to detect failures in datacenters that support critical infrastructures by targeting both the acquisition of monitoring data in a performant way and the minimization of false positives in terms of potential failure detection. The specific approach is the baseline towards decision making and triggering of actions in runtime to ensure service high availability, given that it provides the required data for decision making on time with high accuracy.

Sensitivity analysis in a multidimensional demographic projection model with a two‐sex algorithm*

1993 ◽  
Vol 4 (1) ◽  
pp. 21-36 ◽  
Author(s):  
Peter Ekamper ◽  
Nico Keilman
Keyword(s):  
Sensitivity Analysis ◽  
Projection Model ◽  
Demographic Projection

The Safety Monitoring of Colliery Equipments Based on Data Mining Technology

2011 ◽  
Vol 128-129 ◽  
pp. 731-734
Author(s):  
Shu Fang Zhao ◽  
Li Chao Chen
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Essential Role ◽  
Scientific Information ◽  
Safety Monitoring ◽  
Monitoring Data ◽  
Mining Technology ◽  
Use Of Data ◽  
Stochastic Data

Data mining is the process of abstracting unaware, potential and useful information and knowledge from plentiful, incomplete, noisy, fuzzy and stochastic data. The reliability of colliery equipments takes an essential role in the safety of production. Not only since their continuance of operation, had the accumulation of historical error data of colliery equipments resulted in a mass of surplus data, but also because their lacks of helpful information, which as a result makes colliery managers as well as equipment operators hard to make decisions. Seeing that, we introduced ways here that makes use of data mining technology by processing and analyzing historical monitoring data, recognizing and extracting meaningful patterns so as to provide scientific information for decision-making on the safety of colliery operations, which would help for the forecasting of potential threatens of colliery equipments’ operation, thus, make great contributions to prevent disasters from happening.

scholarly journals Reduction of uncertainties in exposure assessment based on environmental monitoring data

2020 ◽  
Vol 55 ◽  
pp. S81-S88
Author(s):  
M. Bleher ◽  
F. Gering ◽  
U. Stöhlker ◽  
T. Karhunen ◽  
A. Nalbandyan-Schwarz ◽  
...  
Keyword(s):  
Decision Making ◽  
Environmental Monitoring ◽  
Emergency Preparedness ◽  
Early Response ◽  
Assessment Tools ◽  
Dose Assessment ◽  
Monitoring Data ◽  
Special Focus ◽  
Mobile Monitoring ◽  
Emergency Situations

Emergency preparedness and response systems for nuclear and radiological emergencies have to deal with decision-making in situations with relevant uncertainties. Consistent and appropriate protective measures must be decided before, during and after emergency situations. CONFIDENCE WP2 research helps to improve this decision-making process in the urgent response and the early response phase of emergency situations with potential major releases to atmosphere. This paper describes methods to reduce uncertainties in dose assessment for the population using data from stationary and mobile environmental monitoring programs. A special focus is given to identification of the measurement uncertainties of stationary and mobile monitoring systems. Methods to reduce these uncertainties and procedures to optimise mobile monitoring strategies are discussed. A first contribution towards assessing the quality of dose-rate measurements performed by the general population is made. In addition, the paper introduces approaches for advanced dose assessment tools using monitoring data and concepts for identifying critically exposed groups.

scholarly journals Software and hardware module to support decision-making of qualitative abnormal changes presence in sample data based on information metrics

2021 ◽  
pp. 142-151
Author(s):  
Y.E. Shishkin ◽  
◽  
A.V. Skatkov ◽  
Keyword(s):  
Decision Making ◽  
Life Support ◽  
Internal State ◽  
Monitoring Data ◽  
Metrological Reliability ◽  
Sample Distribution ◽  
Leibler Divergence ◽  
Sample Data ◽  
Software And Hardware ◽  
Technical Systems

The key task of society development is to ensure rational use of natural resources and related continuous monitoring of natural and technical systems state. Regarding the growing problems of ensuring operational control of critical infrastructure facilities, tasks of epidemiological and environmental protection, solving the issues of developing new information technologies that meet modern requirements for scientific and practical activities and implementing their software and hardware modules for supporting decision-making on the presence of qualitative anomalous changes in monitoring data aimed at ensuring information and metrological reliability of control systems, becomes critical for the life support of the population. An information technology and a software and hardware module for supporting decision-making on the presence of qualitative abnormal changes in sample data, which are predictors of significant changes in the internal state of monitored objects, natural-technical systems or control devices, are proposed. A method for choosing parametric criteria for the difference in monitoring data using numerical measures of Shannon information entropy and Kullback-Leibler divergence is presented. The use of the developed and demonstrated in practice methodology makes it possible to achieve an increase in the accuracy, convergence and reproducibility of measurements through the use of numerical statistical modeling to obtain a numerical estimate of confident recognition boundaries of a qualitative anomalous change in the shape and shift of the sample distribution of monitoring data, including small samples.

Towards sustainable commercial vendace fisheries in Finland: lessons learned from educating stakeholders for management decision making based on imprecise stock monitoring data

2021 ◽  
Vol 66 ◽  
pp. 25-46
Author(s):  
Timo J. Marjomäki ◽  
Pentti Valkeajärvi ◽  
Tapio Keskinen ◽  
Kari Muje ◽  
Olli Urpanen ◽  
...  
Keyword(s):  
Decision Making ◽  
Lessons Learned ◽  
Monitoring Data ◽  
Management Decision ◽  
Management Decision Making

scholarly journals An Assessment of the Accuracy of a Regional Economic-Demographic Projection Model

Demography ◽  
1984 ◽  
Vol 21 (3) ◽  
pp. 383 ◽  
Author(s):  
Steve H. Murdock ◽  
F. Larry Leistritz ◽  
Rita R. Hamm ◽  
Sean-Shong Hwang ◽  
Banoo Parpia
Keyword(s):  
Projection Model ◽  
Regional Economic ◽  
Demographic Projection
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