scholarly journals A Deep Belief Network and Case Reasoning Based Decision Model for Emergency Rescue

2020 ◽  
Vol 15 (3) ◽  
Author(s):  
Dan Chang ◽  
Rui Fan ◽  
Zitong Sun
Keyword(s):  
Decision Making ◽  
Deep Belief Network ◽  
Economic Losses ◽  
Case Based Reasoning ◽  
Belief Network ◽  
Case Representation ◽  
Emergency Rescue ◽  
Public Emergency ◽  
Decision Making Model ◽  
Case Reasoning

The frequent occurrence of major public emergencies in China has caused significant human and economic losses. To carry out successful rescue operations in such emergencies, decisions need to be made as efficiently as possible. Using earthquakes as an example of a public emergency, this paper combines the Deep Belief Network (DBN) and Case-Based Reasoning (CBR) models to improve the case representation and case retrieval steps in the decision-making process, then designs and constructs a decision-making model. The validity of the model is then verified by an example. The results of this study can be applied to maximize the efficiency of emergency rescue decisions.

Study on Case-Based Reasoning Decision-Making Model

2013 ◽  
Vol 421 ◽  
pp. 636-641
Author(s):  
Yan Sun ◽  
Guang Wei Wang ◽  
Xin Hua He
Keyword(s):  
Decision Model ◽  
Feature Vector ◽  
Matrix Representation ◽  
Case Based Reasoning ◽  
Retrieval Method ◽  
Rule Based ◽  
Case Representation ◽  
Decision Making Model ◽  
Reasoning Mechanism ◽  
Case Based

Beginning with studying the characteristics of the war from a complex system, the limitations of rule-based reasoning approach was discussed, case-based reasoning to the integrated reasoning mechanism was supplemented. In accordance with epistemological, researched on case-based reasoning mechanism of integration and components of decision model, studied feature vector based case representation and matrix representation of non-isomorphic case set, presented a case retrieval method based on feature.

scholarly journals Case-based Reasoning of Damaged Ancient Buildings based on Ontology

2020 ◽  
Vol 165 ◽  
pp. 04007
Author(s):  
Ru Wang ◽  
Wei Chen
Keyword(s):  
Building Damage ◽  
Case Based Reasoning ◽  
Knowledge Reuse ◽  
Case Representation ◽  
Information Interaction ◽  
Knowledge Expression ◽  
Implementation Method ◽  
Definition Of ◽  
Case Reasoning ◽  
Case Based

At present, China has a large number of ancient buildings, and correspondingly, it also faces many problems. Staff of different professions have inconsistent perceptions of ancient buildings, resulting in inability to interact with information. In addition, the ancient architecture case did not carry out efficient reuse of knowledge. Therefore, this article applies ontology to the field of ancient architecture, and proposes the knowledge expression of ancient architecture based on ontology. And SWRL is used to describe the ancient building rules. Secondly, through the application of case-reasoning technology, the reuse of case knowledge of ancient architecture is realized. Ontology-based case representation can provide a unified definition of ancient building knowledge for different participants and lay the foundation for information interaction. Case-based reasoning provides an implementation method for knowledge reuse of ancient building damage cases.

A Hybrid AI-Based Conceptual Decision-Making Model for Sustainable Maintenance Strategy Selection

2019 ◽  
pp. 63-93
Author(s):  
Soumava Boral ◽  
Sanjay K. Chaturvedi ◽  
V. N. A. Naikan ◽  
Ian M. Howard
Keyword(s):  
Decision Making ◽  
Case Based Reasoning ◽  
Strategy Selection ◽  
Maintenance Strategy ◽  
Critical Systems ◽  
Optimal Maintenance ◽  
Complex Decision Making ◽  
Complex Decision ◽  
Decision Making Model ◽  
Hybrid Artificial Intelligence

Selection of optimal maintenance strategy for critical systems/machinery is considered as a complex decision-making task that takes into account several available maintenance alternatives that are evaluated in terms of a set of different conflicting qualitative and quantitative factors. In the last few decades, progress has been made in different sustainable-based decision-making problems, where environmental, social, and economic factors played a pivotal role to arrive at the best decision. In this chapter, a hybrid artificial intelligence (AI)-based conceptual decision-making model is described by taking advantages of both expert system and case-based reasoning methodology to solve sustainable maintenance strategy selection problems. Adding to this, a flowchart of the model is suitably described by hypothetical examples of a sustainable maintenance strategy selection program.

Enhancing Resiliency Feature in Smart Grids through a Deep Learning Based Prediction Model

2020 ◽  
Vol 13 (3) ◽  
pp. 508-518
Author(s):  
Abderrazak Khediri ◽  
Mohamed Ridda Laouar ◽  
Sean B. Eom
Keyword(s):  
Deep Learning ◽  
Prediction Model ◽  
Smart Grids ◽  
Deep Belief Network ◽  
Power Grids ◽  
Economic Losses ◽  
Belief Networks ◽  
Deep Belief Networks ◽  
Belief Network ◽  
Proposed Model

Background: Enhancing the resiliency of electric power grids is becoming a crucial issue due to the outages that have recently occurred. One solution could be the prediction of imminent failure that is engendered by line contingency or grid disturbances. Therefore, a number of researchers have initiated investigations to generate techniques for predicting outages. However, extended blackouts can still occur due to the frailty of distribution power grids. Objective: This paper implements a proactive prediction model based on deep-belief networks to predict the imminent outages using previous historical blackouts, trigger alarms, and suggest solutions for blackouts. These actions can prevent outages, stop cascading failures and diminish the resulting economic losses. Methods: The proposed model is divided into three phases: A, B and C. The first phase (A) represents the initial segment that collects and extracts data and trains the deep belief network using the collected data. Phase B defines the Power outage threshold and determines whether the grid is in a normal state. Phase C involves detecting potential unsafe events, triggering alarms and proposing emergency action plans for restoration. Results: Different machine learning and deep learning algorithms are used in our experiments to validate our proposition, such as Random forest, Bayesian nets and others. Deep belief Networks can achieve 97.30% accuracy and 97.06% precision. Conclusion: The obtained findings demonstrate that the proposed model would be convenient for blackouts’ prediction and that the deep-belief network represents a powerful deep learning tool that can offer plausible results.

Research on Emergency Aid Decision-Making Model for Environmental Emergency Based on Case-Based Reasoning

2014 ◽  
Vol 675-677 ◽  
pp. 206-212
Author(s):  
Ying Ju Zhang
Keyword(s):  
Decision Making ◽  
Nearest Neighbor ◽  
Layer Structure ◽  
Prototype System ◽  
Case Based Reasoning ◽  
Universal Method ◽  
Emergency Decision ◽  
Aid Decision ◽  
Decision Making Model ◽  
Case Based

The paper applied method of case-based reasoning to environmental emergency aid decision-making, and provided a decision-making method based on experience of historical cases to emergency decision-makers. Firstly, a universal method for describing and organizing environmental emergency cases based on three-tier architecture was proposed based on feature analysis of environmental emergency cases. Then a kind of two-layer structure similarity algorithm was designed based on attribute features of environmental emergency cases, which can effectively avoid the defect of traditional Nearest-Neighbor Algorithm. Finally, a CBR prototype system of emergency aid decision-making model was developed and an example of environmental mass incident case was used to testify the practicability of the model. The case example show that the emergency aid decision-making model for environmental emergency based on CBR is applicable in real work.

scholarly journals A NOVEL DECISION-MAKING MODEL FOR SELECTING A CONSTRUCTION PROJECT DELIVERY SYSTEM

2020 ◽  
Vol 26 (7) ◽  
pp. 635-650 ◽  
Author(s):  
Xingyu Zhu ◽  
Xianhai Meng ◽  
Yongqiang Chen
Keyword(s):  
Decision Making ◽  
Delivery System ◽  
Structural Equation ◽  
Construction Project ◽  
Project Delivery ◽  
Equation Modeling ◽  
Case Based Reasoning ◽  
Early Decision ◽  
Decision Making Model ◽  
Bayesian Structural Equation Modeling

It is crucial for the owner of a construction project to select an appropriate project delivery system (PDS) during early decision-making stages of the project. Due to project uncertainty or a lack of project information, the parameters of a PDS are difficult to measure and quantify. Therefore, there are still major challenges to the objective selection of PDSs. This research proposes a novel systematic decision-making model to select the appropriate PDS by using the combination of case-based reasoning (CBR) and robust nonparametric production frontier method. The Bayesian-Structural Equation Modeling (SEM) supported Z-order-m method is interpreted into the case retrieves process of traditional CBR method in order to eliminate the deteriorative internal and external influence for PDS selection. The case study was based on questionnaire survey conducted in China and used to test the validation of the proposed model. The findings reveal that the systematic decision-making model can overcome some problems of the traditional methods and improve the accuracy of PDS selection. As a result, this research has both theoretical and practical implications for the construction industry.

Applying the Multi-Criteria Decision Making Model for Ranking Commercial Banks: The Case of Vietnam

2019 ◽  
pp. 125-133
Author(s):  
Duong Truong Thi Thuy ◽  
Anh Pham Thi Hoang
Keyword(s):  
Decision Making ◽  
Commercial Banks ◽  
Decision Model ◽  
Business Models ◽  
Multi Criteria Decision Making ◽  
Ideal Solution ◽  
Topsis Method ◽  
Decision Making Model

Banking has always played an important role in the economy because of its effects on individuals as well as on the economy. In the process of renovation and modernization of the country, the system of commercial banks has changed dramatically. Business models and services have become more diversified. Therefore, the performance of commercial banks is always attracting the attention of managers, supervisors, banks and customers. Bank ranking can be viewed as a multi-criteria decision model. This article uses the technique for order of preference by similarity to ideal solution (TOPSIS) method to rank some commercial banks in Vietnam.

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