The Experts below are selected from a list of 75480 Experts worldwide ranked by ideXlab platform
Wang Kun - One of the best experts on this subject based on the ideXlab platform.
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Robust Disaster Recovery System Model
Wuhan University Journal of Natural Sciences, 2006Co-Authors: Wang Kun, Su Rui-dan, Li Zeng-xin, Cai Zhen, Zhou Li-huaAbstract:Highly security-critical System should possess features of continuous service. We present a new Robust Disaster Recovery System Model (RDRSM). Through strengthening the ability of safe communications, RDRSM guarantees the secure and reliable command on disaster recovery. Its self-Supervision capability can monitor the integrality and security of disaster recovery System it-self. By 2D and 3D real-time visible platform provided by GIS, GPS and RS, the model makes the using, management and maintenance of disaster recovery System easier. RDRSM possesses predominant features of security, robustness and controllability. And it can be applied to highly security-critical environments such as E-government and bank. Conducted by RDRSM, an important E-government disaster recovery System has been constructed successfully. The feasibility of this model is verified by practice. We especially emphasize the significance of some components of the model, such as risk assessment, disaster recovery planning, System Supervision and robust communication support.
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Study on Disaster Recovery System Model
Computer Science, 2006Co-Authors: Wang KunAbstract:According to the requests of continuous service capability of a certain E-government Experimental and Dem- onstration Project of china,the paper presents a new Robust Disaster Recovery System Model.RDRSM strengthens the ability of safe communication and guarantees the secure command on disaster recovery.Its self-Supervision capabili- ty can monitor the integrality and security of disaster recovery System itself.Using real-time visible platform provided by GIS,GPS and RS,the model can make disaster recovery System easier to use,manage and maintain.The model possesses features of security,robustness,controllability,and can be applied to highly security-critical environments such as e-government and bank.Conducted by RDRSM,an e-government disaster recovery System has been construc- ted over one year.Practice verifies the significance of some components in the model,such as disaster recovery plan- ning,System Supervision,communication support.
Reg - One of the best experts on this subject based on the ideXlab platform.
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Ohio’s Public Water System Supervision Program Revisions
2016Co-Authors: RegAbstract:EPA asked for public comments on its tentative decision to approve Ohio EPA’s revised public water System Supervision program, which complies with federal regulations.
Bruno Berberian - One of the best experts on this subject based on the ideXlab platform.
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Human or not human? Performance monitoring ERPs during human agent and machine Supervision
NeuroImage, 2019Co-Authors: Bertille Somon, Aurélie Campagne, Arnaud Delorme, Bruno BerberianAbstract:Performance monitoring is a critical process which allows us to both learn from our own errors, and also interact with other human beings. However, our increasingly automated world requires us to interact more and more with automated Systems, especially in risky environments. The present EEG study aimed at investigating and comparing the neuro-functional correlates associated with performance monitoring of an automated System and a human agent using a vertically-oriented arrowhead version of the flanker task. Given the influence of task difficulty on performance monitoring, two levels of difficulty were considered in order to assess their impact on Supervision activity. A large N2 P3 complex in fronto-central regions was observed for both human agent error detection and System error detection during Supervision. Using a cluster-based permutation analysis, a significantly decreased P3-like component was found for System compared to human agent error detection. This variation is in line with various psychosocial behavioral studies showing a difference between human-human and human-machine interactions, even though it was not clearly anticipated. Finally, the activity observed during error detection was significantly reduced in the difficult condition compared to the easy one, for both System and human agent Supervision. Overall, this study is a first step towards the characterization of the neurophysio-logical correlates underlying System Supervision, and a better understanding of their evolution in more complex environments. To go further, these results need to be replicated in other experiments with various paradigms to assess the robustness of the pattern and decrease during System Supervision.
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Performance Monitoring Applied to System Supervision.
Frontiers in Human Neuroscience, 2017Co-Authors: Bertille Somon, Aurélie Campagne, Arnaud Delorme, Bruno BerberianAbstract:Nowadays, automation is present in every aspect of our daily life and has some benefits. Nonetheless, empirical data suggest that traditional automation has many negative performance and safety consequences as it changed task performers into task supervisors. In this context, we propose to use recent insights into the anatomical and neurophysiological substrates of action monitoring in humans, to help further characterize performance monitoring during System Supervision. Error monitoring is critical for humans to learn from the consequences of their actions. A wide variety of studies have shown that the error monitoring System is involved not only in our own errors, but also in the errors of others. We hypothesize that the neurobiological correlates of the self-performance monitoring activity can be applied to System Supervision. At a larger scale, a better understanding of System Supervision may allow its negative effects to be anticipated or even countered. This review is divided into three main parts. First, we assess the neurophysiological correlates of self-performance monitoring and their characteristics during error execution. Then, we extend these results to include performance monitoring and error observation of others or of Systems. Finally, we provide further directions in the study of System Supervision and assess the limits preventing us from studying a well-known phenomenon: the Out-Of-the-Loop (OOL) performance problem.
Ricard Tomas - One of the best experts on this subject based on the ideXlab platform.
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CCWI2017: F74 'Data Setup for Water Distribution System Supervision'
2017Co-Authors: Ramon Pérez, Sergi Grau, Víctor Jiménez, Xavier Martínez, Ricard TomasAbstract:The availability of on-line data coming from the water distribution Systems (WDS) allows the monitoring of such critical infrastructures. Nevertheless the huge amount of data that come to the control centre implies an enormous processing challenge [4]. The use of data avoids any physical theory and relies on statistical correlations and inferences. Nevertheless the previous efforts in modelling the networks encourage the companies to fusion information coming from both sources. Models help validating data and data update these models. This paper presents the first stage of an ongoing project focused in the integration of data and models. Data are collected, harmonised and validated using the models so that they will be used in following stages of the project for the Supervision of the WDS (water balance and quality). A tool is being developed in R where the different modules will be integrated.
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Data setup for water distribution System Supervision
2017Co-Authors: Ramon Pérez Magrané, Sergi Grau, Víctor Jiménez, Xavier Martínez, Ricard TomasAbstract:The availability of on-line data coming from the water distribution Systems (WDS) allows the monitoring of such critical infrastructures. Nevertheless the huge amount of data that come to the control centre implies an enormous processing challenge [4]. The use of data avoids any physical theory and relies on statistical correlations and inferences. Nevertheless the previous efforts in modelling the networks encourage the companies to fusion information coming from both sources. Models help validating data and data update these models. This paper presents the first stage of an ongoing project focused in the integration of data and models. Data are collected, harmonised and validated using the models so that they will be used in following stages of the project for the Supervision of the WDS (water balance and quality). A tool is being developed in R where the different modules will be integrated.
Ogwdw - One of the best experts on this subject based on the ideXlab platform.
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Public Water System Supervision Program Water Supply Guidance Manual
2015Co-Authors: OgwdwAbstract:Drinking water policy memos, regulation and guidance to assist states and pubic water Systems in implementing the Safe Drinking Water Act.
