IEEE Transactions on Systems Man Cybernetics-SystemsIEEE 系统、人与控制论汇刊：系统

IEEE Transactions on Systems Man Cybernetics-Systems(英文缩写 IEEE T SYST MAN CY-S),ISSN 2168-2216,eISSN 2168-2232,中文译名:IEEE 系统、人与控制论汇刊：系统 是一本学术期刊。本页汇总该期刊的最新影响因子、分区信息以及最新收录于 PubMed 的文献,帮助您快速了解期刊全貌。

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IEEE Transactions on Systems Man Cybernetics-Systems 最新收录文献

1. JCR分区: Q1 CAS分区: B1 影响因子: 8.4

   1. A Lightweight Social Computing Approach to Emergency Management Policy Selection.

   PMID:

   34192095

   作者:

   Mingsheng Tang, Haibin Zhu, Xinjun Mao

   期刊:

   IEEE transactions on systems, man, and cybernetics. Systems PubMed

   DOI:

   10.1109/TSMC.2015.2484281

   日期:

   2016-08-01

   In order to select effective policies for emergency management in a timely manner, this paper proposes an agile and lightweight social computing approach to facilitating policy selection, evaluation, and adjustment relative to emergency management in both quantitative and qualitative ways. The approach consists of three components represented as PZE: 1) (P) emergency management policy selecting; 2) (Z) modeling artificial societies with the zombie-city model (a general and formal artificial society model); and 3) (E) policy evaluation. The formal specification of the zombie-city model and rigorous expressions of scenarios enable rigorous description and formal reasoning of an artificial society. A feedback loop of this approach supports the iterative adjustment of emergency management policies and the creation of more effective policies. This approach is verified by applying it to a case of an infectious disease transmission with quantitative evaluations, qualitative reasoning and analysis, and iterative adjustments. Results indicate effective emergency management policies can be established with the approach in an iterative way. In contrast with existing research, our proposed approach offers the benefits of being simple, general, rapidly adaptive to changes, and low cost.

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2. JCR分区: Q1 CAS分区: B1 影响因子: 8.4

   2. RE-PLAN: An Extensible Software Architecture to Facilitate Disaster Response Planning.

   PMID:

   25419503

   作者:

   Martin O'Neill, Armin R Mikler, Saratchandra Indrakanti, Chetan Tiwari, Tamara Jimenez

   期刊:

   IEEE transactions on systems, man, and cybernetics. Systems PubMed

   DOI:

   10.1109/TSMC.2014.2332137

   日期:

   2014-12-01

   Computational tools are needed to make data-driven disaster mitigation planning accessible to planners and policymakers without the need for programming or GIS expertise. To address this problem, we have created modules to facilitate quantitative analyses pertinent to a variety of different disaster scenarios. These modules, which comprise the REsponse PLan ANalyzer (RE-PLAN) framework, may be used to create tools for specific disaster scenarios that allow planners to harness large amounts of disparate data and execute computational models through a point-and-click interface. Bio-E, a user-friendly tool built using this framework, was designed to develop and analyze the feasibility of ad hoc clinics for treating populations following a biological emergency event. In this article, the design and implementation of the RE-PLAN framework are described, and the functionality of the modules used in the Bio-E biological emergency mitigation tool are demonstrated.

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3. JCR分区: Q1 CAS分区: B1 影响因子: 8.4

   3. Automated Cognitive Health Assessment Using Smart Home Monitoring of Complex Tasks.

   PMID:

   25530925

   作者:

   Prafulla N Dawadi, Diane J Cook, Maureen Schmitter-Edgecombe

   期刊:

   IEEE transactions on systems, man, and cybernetics. Systems PubMed

   DOI:

   10.1109/TSMC.2013.2252338

   日期:

   2013-11-01

   One of the many services that intelligent systems can provide is the automated assessment of resident well-being. We hypothesize that the functional health of individuals, or ability of individuals to perform activities independently without assistance, can be estimated by tracking their activities using smart home technologies. In this paper, we introduce a machine learning-based method for assessing activity quality in smart homes. To validate our approach we quantify activity quality for 179 volunteer participants who performed a complex, interweaved set of activities in our smart home apartment. We observed a statistically significant correlation (r=0.79) between automated assessment of task quality and direct observation scores. Using machine learning techniques to predict the cognitive health of the participants based on task quality is accomplished with an AUC value of 0.64. We believe that this capability is an important step in understanding everyday functional health of individuals in their home environments.

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