Situation Assessment Model Research Articles

Support for context-aware monitoring in home healthcare

This paper tackles the problem of supporting independent living and well-being for people that live in their homes and have no critical chronic condition. The paper assumes the presence of a monitoring system equipped with a pervasive sensor network and a non-monotonic reasoning engine. The rich set of sensors that can be used for monitoring in home environments and their sheer number make it quite complex to provide a correct interpretation of collected data for a particular patient. For this reason, we introduce a logic-based context model for situation assessment combined with high level declarative feedback policy specification, and we use logic programming techniques to reason about different pieces of knowledge for prevention.

A computational model for evaluating the effects of attention, memory, and mental models on situation assessment of nuclear power plant operators

Operators in nuclear power plants have to acquire information from human system interfaces (HSIs) and the environment in order to create, update, and confirm their understanding of a plant state, as failures of situation assessment may cause wrong decisions for process control and finally errors of commission in nuclear power plants. A few computational models that can be used to predict and quantify the situation awareness of operators have been suggested. However, these models do not sufficiently consider human characteristics for nuclear power plant operators. In this paper, we propose a computational model for situation assessment of nuclear power plant operators using a Bayesian network. This model incorporates human factors significantly affecting operators’ situation assessment, such as attention, working memory decay, and mental model. As this proposed model provides quantitative results of situation assessment and diagnostic performance, we expect that this model can be used in the design and evaluation of human system interfaces as well as the prediction of situation awareness errors in the human reliability analysis.

A situation assessment approach using support vector machines as a learning tool

In order to assess a situation and support decision makers' awareness for the situation, this study first proposes a situation assessment model with mathematical description. It then develops a Support Vector Machine based assessment approach, which has the ability to learn the rules from the previous assessment results and generate necessary warnings for a situation. Finally, a set of experiments is conducted to illustrate and validate the proposed approach.

06/02136 An analytic model for situation assessment of nuclear power plant operators based on Bayesian inference: Kim, M. C. and Seong, P. H. Reliability Engineering & System Safety, 2006, 91, (3), 270–282.

06/02136 An analytic model for situation assessment of nuclear power plant operators based on Bayesian inference: Kim, M. C. and Seong, P. H. Reliability Engineering & System Safety, 2006, 91, (3), 270–282.

An analytic model for situation assessment of nuclear power plant operators based on Bayesian inference

Simulation-based human reliability analysis (HRA) methods such as IDAC seem to provide a new direction for the development of advanced HRA methods. In such simulation-based HRA methods, the simulation model for the situation assessment of nuclear power plant (NPP) operators is essential, especially for addressing the issue of errors-of-commission (EOCs). Therefore, we propose an analytic model for the situation assessment of NPP operators based on Bayesian inference. The proposed model is found to be able to address several important features of the situation assessment of NPP operators, and is expected to provide good approximations to some parts of the situation assessment. A comparison with an existing model and identification of several other features of the situation assessment of NPP operators that should be further addressed are also provided.

ASA and its application to multi-criteria decision making

Aggregation plays an important role in many applications related to the development of intelligent systems. In a fuzzy environment the existing aggregation operators are generally the t-norm, t-conorm, mean operators, Yager's operator and γ-operator. However, these aggregation operators do not properly reflect the situation in the aggregation process. That is, these types of aggregation operators are independent of the aggregation situation. In order to solve these problems we propose a new aggregation method to reflect the situation in the aggregation process. It is the aggregation based on situation assessment (ASA) method. It consists of the situation assessment model (SAM) and the ASA algorithm. In this ASA method, the SAM is utilized to reflect the situation in the aggregation process. This model generates the parameter, which is controlled by decision-maker. It indicates the current degree of aggregation situation. Therefore, our method can be adapted to certain situations by using the parameter. In this paper, ASA method is applied to the multi-criteria decision making environment.

A new aggregation method in a fuzzy environment

Information aggregation provides a starting point for the ability to make useful inferences from large collections of data, and so it plays an important role in many applications related to the development of intelligent systems. In a fuzzy environment, the existing aggregation operators are generally the t-norm, t-conorm, mean operators, Yager's operator and γ-operator. However, these aggregation operators do not reflect the situation in the aggregation process, i.e., these types of aggregation operators are independent of the aggregation situation. In order to solve these problems, we propose a new aggregation method to reflect the situation in the aggregation process. It is the aggregation based on situation assessment (ASA) method. It consists of the situation assessment model (SAM) and the ASA algorithm. In this ASA method, the SAM is utilized to reflect the situation in the aggregation process. This model generates the parameter, which is controlled by the decision maker. It indicates the current degree of aggregation situation. Therefore, our method can be adapted to certain situations by using the parameter.

A computational situation assessment model for nuclear power plant operations

This paper presents a computational situation assessment (SA) model and a model-based SA metric for nuclear power plant operations. The model and metric development starts with a definition of the plant operator's SA centered decision making behavior. A computational SA model and a model-based SA metric are then developed to quantify and measure operator SA. Using the SA model as a core, we further develop an integrated operator/plant model that provides for explicit representation of the operator's fundamental functions of information processing, situation assessment, and decision making in a closed-loop plant/operator simulation environment. We evaluate the model and metric in a model-based simulation of a selected emergency scenario, and a model-based analysis of a range of contemplated nuclear power plant automation/aiding options.