Expert Knowledge Integration—A Systematic Approach for Multi-stakeholder Innovation

2017 ◽  
pp. 113-129 ◽  
Author(s):  
Anu Suominen ◽  
Sari Mäenpää ◽  
Rainer Breite
Keyword(s):  
Knowledge Integration ◽  
Systematic Approach ◽  
Expert Knowledge ◽  
Multi Stakeholder

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.

scholarly journals Feedback Loops and Facilitation: Catalyzing Transformational Multi-Stakeholder Refugee Response Partnerships

2021 ◽  
Vol 13 (21) ◽  
pp. 11705
Author(s):  
Jaime Moreno-Serna ◽  
Teresa Sánchez-Chaparro ◽  
Leda Stott ◽  
Javier Mazorra ◽  
Ruth Carrasco-Gallego ◽  
...  
Keyword(s):  
Expert Knowledge ◽  
Clean Energy ◽  
Feedback Loops ◽  
Northern Ethiopia ◽  
Refugee Camps ◽  
Dyadic Relationships ◽  
The Sustainable Development ◽  
Stakeholder Partnerships ◽  
Multi Stakeholder ◽  
Translation Service

Global policies such as the recent ‘Comprehensive Refugee Response Framework’ call for a profound transformation in refugee response. To this end, collaboration with non-traditional humanitarian actors, particularly the private sector has been advocated. The application of new multi-stakeholder partnerships that transcend traditional dyadic relationships have been commended by practitioners for their ability to create stable services and markets in refugee camps. However, the adaptation of multi-stakeholder partnership models to the novelties of refugee response and the dynamics among partners in these complex arrangements requires more attention. This paper explores how the creation and development of multi-stakeholder partnerships can maximize the transformational potential of collaboration for refugee response, ensure the stakeholder diversity needed to provide basic services on a stable basis, and provide a facilitation function that supports the partnership. Using an action-case methodology, the focus of the article is on the Alianza Shire, Spain’s first multi-stakeholder partnership for humanitarian action, which was established to provide energy to refugee camps and host communities in refugee camps in northern Ethiopia. Our findings suggest that i) the active participation of aid agencies in the co-creation process of a multi-stakeholder partnership may increase the transformational potential of refugee response, ii) feedback loops and the consolidation of internal learning are essential practices for the effective management of complex multi-stakeholder partnerships, and iii) the facilitator plays a critical and underexplored role in refugee response collaborative arrangements. In addition, sustainability-oriented university centers may possess a particular capacity for nurturing the transformational potential of multi-stakeholder refugee response partnerships by generating ‘safe spaces’ that foster trust-building, providing a cross-sector ‘translation’ service, and affording the legitimacy and expert knowledge required to conduct learning processes. We believe that the theoretical and practical implications of our research may contribute to the effective fulfilment of the Sustainable Development Goals, specially, SDG7 (Affordable and Clean Energy) and SDG17 (Partnership for the Goals).

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

scholarly journals Perturbation robustness analyses reveal important parameters in variant interpretation pipelines

2020 ◽  
Author(s):  
Yaqiong Wang ◽  
Aashish N. Adhikari ◽  
Uma Sunderam ◽  
Mark N. Kvale ◽  
Robert J. Currier ◽  
...  
Keyword(s):  
Systematic Approach ◽  
Expert Knowledge ◽  
Variant Interpretation ◽  
Limited Data ◽  
Inborn Errors ◽  
General Application ◽  
Impact Prediction ◽  
Prediction Tools ◽  
Genome Interpretation ◽  
Pipeline Design

AbstractMotivationGenome sequencing is being used routinely in clinical and research applications, but subsequent variant interpretation pipelines can vary widely. A systematic approach for exploring parameter choices and selection plays an important role in designing robust pipelines for specific clinical applications.ResultsWe present a framework to be applied in scenarios with limited data whereby expert knowledge informs pipeline refinement. Starting from initial reference variant interpretation pipelines with commonly used parameters, we derived pipelines by perturbing the parameters one by one to determine which parameters can yield meaningful changes in a pipeline’s performance. We updated the reference pipeline by fixing the value of parameters which have small impact on the pipeline’s performance. Then we conducted new rounds of perturbation as the process converged, yielding a stable pipeline which is robust. We applied the framework for genetic disease prediction in de-identified exomes from a cohort of 138 individuals with rare Mendelian inborn errors of metabolism (IEMs) and systematically explored how perturbing different parameters affected the pipeline’s sensitivity and specificity. For this application, we perturbed commonly used parameters in variant interpretation pipelines, including choices of genes, variant callers, transcript models, databases of allele frequencies, databases of curated disease variants, and tools for variant impact prediction. Our analyses showed that choice of variant callers, variant impact prediction tools, MAF threshold, and MAF databases can meaningfully alter results from a pipeline. This work informs the development of exome analysis pipelines designed for newborn metabolic disorder screening and suggests the general application of perturbation analysis in genome interpretation pipeline design.

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

Organisational drivers of capabilities for multi-stakeholder dialogue and knowledge integration

2013 ◽  
Vol 13 (2) ◽  
pp. 107-117 ◽  
Author(s):  
M. Veldhuizen ◽  
V. Blok ◽  
D. Dentoni
Keyword(s):  
Knowledge Integration ◽  
Learning Theories ◽  
Stakeholder Dialogue ◽  
Business Cases ◽  
Key Drivers ◽  
Social Environmental ◽  
Multi Stakeholder ◽  
Stakeholder Interactions ◽  
Corporate Social

Business and policy actors increasingly make use of multi-stakeholder interactions (MSI) as a corporate social responsibility strategy to understand, influence, harmonise and meet stakeholders’ social, environmental and financial expectations and so to create value. While many researchers and practitioners have recently described the role of MSI for sustainable innovation and development, little is known about how organisations can develop a capability to effectively create and maintain a dialogue with stakeholders and learn from them. The paper explores the organisational characteristics driving two key capabilities needed for effective MSI: stakeholder dialogue and knowledge integration. Based on the empirical evidence from four business cases, the research follows an explorative approach building upon stakeholder and organisational learning theories. Findings indicate that the ‘involvement of senior management and employees’, ‘open culture’, ‘vision towards sustainability’ and ‘hierarchical structure’ are key drivers of stakeholder dialogue and knowledge integration capabilities.

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