Multi-Factor Evaluation of Natural Person Solvency by Expert Knowledge Integration

2020 ◽  
Vol 52 (3) ◽  
pp. 65-76
Author(s):  
Aliaga Alekper Ogly Aliyev
Keyword(s):  
Knowledge Integration ◽  
Expert Knowledge ◽  
Natural Person ◽  
Factor Evaluation

Expert knowledge integration to model complex food processes. Application on the camembert cheese ripening process

2011 ◽  
Vol 38 (9) ◽  
pp. 11804-11812 ◽  
Author(s):  
M. Sicard ◽  
C. Baudrit ◽  
M.N. Leclerc-Perlat ◽  
P.H. Wuillemin ◽  
N. Perrot
Keyword(s):  
Knowledge Integration ◽  
Expert Knowledge ◽  
Ripening Process ◽  
Cheese Ripening ◽  
Model Complex ◽  
Camembert Cheese ◽  
Food Processes

Development and Analysis of an Enhanced Multi-Expert Knowledge Integration System for Designing Context-Aware Ubiquitous Learning Contents

2018 ◽  
Vol 16 (4) ◽  
pp. 31-53 ◽  
Author(s):  
Gwo-Haur Hwang ◽  
Beyin Chen ◽  
Shiau-Huei Huang
Keyword(s):  
Knowledge Acquisition ◽  
Real World ◽  
Domain Knowledge ◽  
Knowledge Integration ◽  
Expert Knowledge ◽  
Context Aware ◽  
Ubiquitous Learning ◽  
Integration System ◽  
Degree Of Acceptance ◽  
Multiple Experts

This article describes how in context-aware ubiquitous learning environments, teachers must plan a theme and design learning contents to provide complete knowledge for students. Knowledge acquisition, which is an approach for helping people represent and organize domain knowledge, has been recognized as a potential way of guiding teachers to develop real-world context-related learning contents. However, previous studies failed to address the issue that the learning contents provided by multiple experts or teachers might be redundant or inconsistent; moreover, it is difficult to use the traditional knowledge acquisition method to fully describe the complex real-world contexts and the learning contents. Therefore, in this article, a multi-expert knowledge integration system with an enhanced knowledge representation approach and Delphi method has been developed. From the experimental results, it is found that the teachers involved had a high degree of acceptance of the system. They believe that it can unify the knowledge of many teachers.

scholarly journals Fuzzy Filtering of Sensors Signals in Manufacturing Systems with Time Constraints

2010 ◽  
Vol 5 (3) ◽  
pp. 362 ◽  
Author(s):  
Anis M’halla ◽  
Nabil Jerbi ◽  
Simon Collart Dutilleul ◽  
Etienne Craye ◽  
Mohamed Benrejeb
Keyword(s):  
Fuzzy Systems ◽  
Manufacturing Systems ◽  
Knowledge Integration ◽  
Expert Knowledge ◽  
Time Constraints ◽  
Quality And Safety ◽  
Job Shops ◽  
New Approach ◽  
Robustness Property ◽  
Filtering Approach

The presented work is dedicated to the supervision of manufacturing job-shops with time constraints. Such systems have a robustness property towards time disturbances. The main contribution of this paper is a fuzzy filtering approach of sensors signals integrating the robustness values. This new approach integrates a classic filtering mechanism of sensors signals and fuzzy logic techniques. The strengths of these both techniques are taken advantage of the avoidance of control freezing and the capability of fuzzy systems to deal with imprecise information by using fuzzy rules. Finally, to demonstrate the effectiveness and accuracy of this new approach, an example is depicted. The results show that the fuzzy approach allows keeping on producing, but in a degraded mode, while providing the guarantees of quality and safety based on expert knowledge integration.

A protocol for the conceptualisation of an agro-ecosystem to guide data acquisition and analysis and expert knowledge integration

2012 ◽  
Vol 38 ◽  
pp. 104-116 ◽  
Author(s):  
Nathalie Lamanda ◽  
Sébastien Roux ◽  
Sylvestre Delmotte ◽  
Anne Merot ◽  
Bruno Rapidel ◽  
...  
Keyword(s):  
Data Acquisition ◽  
Knowledge Integration ◽  
Expert Knowledge

A multi-source data feature fusion and expert knowledge integration approach on lithium-ion battery anomaly detection

2021 ◽  
pp. 1-14
Author(s):  
Yudong Wang ◽  
Xiwei Bai ◽  
Chengbao Liu ◽  
Jie Tan
Keyword(s):  
Anomaly Detection ◽  
Lithium Ion Battery ◽  
Network Architecture ◽  
Knowledge Integration ◽  
Expert Knowledge ◽  
Feature Fusion ◽  
Lithium Ion ◽  
Data Driven ◽  
Power Sources ◽  
Source Data

Abstract To meet voltage and capability needs, batteries are grouped into packs, as power sources. Abnormal ones in a pack will lead to partial heating and reduced available life, so removing anomalies out during manufacturing is of great significance. The conventional methods to detect abnormal batteries mainly rely on grading systems and manual operations. Current data-driven methods use statistical, machine learning and neural network approaches, building models, then applying them on the unlabeled. However, both cannot make full use of multiple source data, and expert knowledge. Therefore, how to use these multi-source data and knowledges to improve the effect of battery anomaly detection process has become a research focus. We put forward a data-driven multi-source data feature fusion and expert knowledge integration (FFEKI) network architecture which follows encoder-decoder structure with multiple integration units and a corresponding joint loss function. First, we collect multi-source data, and obtain fusion features. Then, we refine filters from expert knowledges. By this way, supervisory knowledges are integrated into our network by integration units. We evaluate our scheme by sets of experiments comparing with most widely used approaches on real manufacturing data. Results show that FFEKI obtains a maximum 100% anomaly detection rate (ADR). Meanwhile, when the number of detection T is greater than the actual number of anomalies in the sample set, our method can achieve full ADR faster. It is concluded that the proposed FFEKI achieves effective performance on power lithium-ion battery anomaly detection.

scholarly journals Multi-criteria scheduling: an agent-based approach for expert knowledge integration

2011 ◽  
Vol 16 (4) ◽  
pp. 369-383 ◽  
Author(s):  
Christian Grimme ◽  
Joachim Lepping ◽  
Uwe Schwiegelshohn
Keyword(s):  
Knowledge Integration ◽  
Expert Knowledge ◽  
Agent Based
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