Research on Combat Effectiveness Evaluation of Radar EW System Based on Bayesian Network

2011 ◽  
Vol 204-210 ◽  
pp. 1697-1700 ◽  
Author(s):  
Yu Jie Zheng
Keyword(s):  
Bayesian Network ◽  
Evaluation Model ◽  
Effectiveness Evaluation ◽  
Analysis Model ◽  
Key Factors ◽  
Bayesian Network Model ◽  
Simulation Data ◽  
Factors Analysis ◽  
Combat Effectiveness ◽  
Hidden Knowledge

Radar EW system combat effectiveness evaluation is a essential link to Radar system Demonstration, mainly give service to selection, optimization and key factors analysis of Weapon equipment scheme. In this paper, we introduce the Bayesian network model into the area of Radar EW system combat effectiveness evaluation and put forward the concept of combat effectiveness evaluation model based on Bayesian network. The ability to express complex relationship, the ability to express the uncertainty of probability, and the reasoning functions. By learning from Expertise and Simulation data, excavating the hidden knowledge included in both of them, we can build the combat efficiency Analysis model, and then carry out efficient analysis.

Importance Degree of Influencing Factors on Cloud Service Composition Flexibility Based on Bayesian Network

2014 ◽  
Vol 988 ◽  
pp. 745-750
Author(s):  
Kai Zhang ◽  
Xuan Guo Xu
Keyword(s):  
Bayesian Network ◽  
Influencing Factors ◽  
Network Model ◽  
Service Composition ◽  
Cloud Service ◽  
Key Factors ◽  
Bayesian Network Model ◽  
Cloud Service Composition

In this paper, the cloud service composition flexibility and its influencing factors were analyzed. Because the cloud service composition has too many uncertainties, a Bayesian network model was built to analyze the importance degree of the influencing factors of cloud service composition flexibility, and then the key factors were identified. This paper offered the groundwork for the subsequent influencing factors monitoring and cloud service composition flexibility measuring.

Research on Operational Effectiveness Evaluation Based on Interval Number and Grey Decision Making

2014 ◽  
Vol 912-914 ◽  
pp. 1702-1705
Author(s):  
Xue Zheng Zhu ◽  
Dong Kuang ◽  
Jin Gu ◽  
Kun Lin Nie
Keyword(s):  
Decision Making ◽  
Fuzzy Number ◽  
Comprehensive Evaluation ◽  
Evaluation Model ◽  
Interval Number ◽  
Effectiveness Evaluation ◽  
Operational Effectiveness ◽  
Model Based ◽  
Combat Effectiveness

During the combat effectiveness evaluation,some problems such as the precise number,interval number and the language fuzzy number exist in the evaluation values.To solve these problems,this paper introduces the interval number and grey decision making principle and brings forward a operational effectiveness evaluation model based on the interval number and grey decision making.This model can truly reflect the comprehensive evaluation value of the weapon equipment operational effectiveness.

scholarly journals Efficiency of Probabilistic Network Model for Assessment in E-Learning System

2020 ◽  
Vol 9 (3) ◽  
pp. 562-566
Keyword(s):  
Bayesian Network ◽  
Network Model ◽  
Graphical Representation ◽  
Evaluation Model ◽  
Random Order ◽  
Learning System ◽  
Bayesian Network Model ◽  
Sequential Approach ◽  
Knowledge Resources ◽  
E Learning

The knowledge acquirement by the learner is a major assignment of an E-Learning framework. Evaluation is required in order to adapt knowledge resources and task to learner ability. Assessment provides learner’s an approach to evaluate the skills gained through the e-learning domain they are accessing. A dissimilar method can be used to assess the information acquirement, such as probabilistic Bayesian Network model. A Bayesian Network is a graphical representation of the probabilistic relationships of a complex system. This network can be used for reasoning with uncertainty. Bayesian Network is the most challenging task in e-learning system as learner evaluation model are an element of uncertainty. In this paper the current proposed scheme is constructed on Bayesian Network to deduce the stage of knowledge possessed by the learner. It also proposes type of assessment to identify the knowledge whatever the learner identifies. Throughout the assessment, it can be performed by two approaches namely Sequential and Random. In Sequential approach, questions can be displayed on the learner machine in sequential order. In Random approach, questions can be displayed on the learner machine in random order. However, both have their inherent limitations. Questions that are considered to be answered easily by the learner may also be presented to the learner who is not desirable. This system determined on the illustration of Bayesian Network model and algorithm for inference about learner’s knowledge. The Bayesian Network model was efficiently implemented for three levels of learner called Higher Learners (HL), Regular Learners (RL) and Irregular Learners (IL) for learner’s assessment and was successfully implemented with 81.1% of probabilities for learner’s assessment.

Analysis of driver fatigue causations based on the Bayesian network model

SIMULATION ◽  
2016 ◽  
Vol 93 (7) ◽  
pp. 553-565 ◽  
Author(s):  
Longhui Gang ◽  
Xiaolin Song ◽  
Mingheng Zhang ◽  
Baozhen Yao ◽  
Liping Zhou
Keyword(s):  
Bayesian Network ◽  
Network Model ◽  
Research Field ◽  
Structure Design ◽  
Parameter Determination ◽  
Analysis Model ◽  
Source Information ◽  
Bayesian Network Model ◽  
Driver Fatigue ◽  
Causation Analysis

Driver fatigue is the major reason for severe traffic accidents. At present, the driver’s driving state evaluation, based on multi-source information fusion, has become a hotspot in the research field of vehicle safety assistant driving. The purpose of this paper is to build a Bayesian network model for driver fatigue causation analysis considering several visual cues, such as Percentage of Eyelid Closure over the Pupil over Time, Average Eye Closure Speed, etc. The proposed method was divided into three stages, that is, variables analysis, model structure design, and model parameter determination. Finally, the presented model and algorithm were illustrated with a simulation experiment and conclusions were inferred from the experiment data analysis.

Evaluation Model of Combat Effectiveness Based on MAS

2015 ◽  
Vol 734 ◽  
pp. 443-446
Author(s):  
Bo Zhang ◽  
Da Wei Zhao ◽  
Xue Zhao
Keyword(s):  
Complex System ◽  
Field Theory ◽  
System Theory ◽  
Potential Field ◽  
Evaluation Model ◽  
Theory Model ◽  
Effectiveness Evaluation ◽  
Agent Model ◽  
Combat Effectiveness ◽  
Multi Agent

This paper presents ideas that using the complex system theory to achieve combat effectiveness evaluation, and constructs an combat agent model and a potential field theory model, then take an example to accomplish the combat effectiveness evaluation based on multi-agent.

scholarly journals A Unified View of Causal and Non-causal Feature Selection

2021 ◽  
Vol 15 (4) ◽  
pp. 1-46
Author(s):  
Kui Yu ◽  
Lin Liu ◽  
Jiuyong Li
Keyword(s):  
Feature Selection ◽  
Bayesian Network ◽  
Synthetic Data ◽  
Selection Methods ◽  
Bayesian Network Model ◽  
Real World Data ◽  
Feature Sets ◽  
Unified View ◽  
Optimal Feature ◽  
Different Levels

In this article, we aim to develop a unified view of causal and non-causal feature selection methods. The unified view will fill in the gap in the research of the relation between the two types of methods. Based on the Bayesian network framework and information theory, we first show that causal and non-causal feature selection methods share the same objective. That is to find the Markov blanket of a class attribute, the theoretically optimal feature set for classification. We then examine the assumptions made by causal and non-causal feature selection methods when searching for the optimal feature set, and unify the assumptions by mapping them to the restrictions on the structure of the Bayesian network model of the studied problem. We further analyze in detail how the structural assumptions lead to the different levels of approximations employed by the methods in their search, which then result in the approximations in the feature sets found by the methods with respect to the optimal feature set. With the unified view, we can interpret the output of non-causal methods from a causal perspective and derive the error bounds of both types of methods. Finally, we present practical understanding of the relation between causal and non-causal methods using extensive experiments with synthetic data and various types of real-world data.

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