Erratum to “An approach to use linguistic and model-based fuzzy expert knowledge for the analysis of MRT images”

2001 ◽  
Vol 19 (13) ◽  
pp. 1021
Author(s):  
J. Hiltner ◽  
M. Fathi ◽  
B. Reusch
Keyword(s):  
Expert Knowledge ◽  
Model Based

scholarly journals A Case Studies Approach to the Analysis of Profiling and Framing Structures for Pervasive Information Systems

2012 ◽  
Vol 4 (2) ◽  
pp. 1-18 ◽  
Author(s):  
José Eduardo Fernandes ◽  
Ricardo J. Machado ◽  
João Á. Carvalho
Keyword(s):  
Information Systems ◽  
Case Studies ◽  
Software Design ◽  
Expert Knowledge ◽  
Structural Approach ◽  
Technological Changes ◽  
Design And Development ◽  
Structure Approach ◽  
Model Based ◽  
Widespread Adoption

Model-Based/Driven Development (MDD) constitutes an approach to software design and development that potentially contributes to: concepts closer to domain and reduction of semantic gaps, automation and less sensitivity to technological changes, and the capture of expert knowledge and reuse. The widespread adoption of pervasive technologies as basis for new systems and applications lead to the need of effectively design pervasive information systems that properly fulfil the goals they were designed for. This paper presents a profiling and framing structure approach for the development of Pervasive Information Systems (PIS). This profiling and framing structure allows the organization of the functionality that can be assigned to computational devices in a system and of the corresponding development structures and models, being. The proposed approach enables a structural approach to PIS development. The paper also presents two case studies that allowed demonstrating the applicability of the approach.

A MODEL BASED ON INFLUENCE DIAGRAMS FOR MULTI-CRITERIA DECISION-MAKING

2012 ◽  
Vol 21 (04) ◽  
pp. 1250018 ◽  
Author(s):  
KARIMA SEDKI ◽  
VÉRONIQUE DELCROIX
Keyword(s):  
Decision Making ◽  
Decision Maker ◽  
Decision Problem ◽  
Expert Knowledge ◽  
External Factors ◽  
Influence Diagrams ◽  
Multi Criteria Decision Making ◽  
Model Based ◽  
Decision Variables ◽  
Other Information

In this paper, we focus on multi-criteria decision-making problems. We propose a model based on influence diagrams; this model is able to handle uncertainty, represent interdependencies among the different decision variables and facilitate communication between the decision-maker and the analyst. The particular structure of the proposed model makes it possible to take into account the alternatives described by an attribute set, the decision-maker's characteristics and preferences, and other information (e.g., internal or external factors) that influence the decision. Modeling the decision problem in terms of influence diagrams requires a lot of work to gather expert knowledge. However, once the model is built, it can be easily and efficiently used for different instances of the decision problem. In fact, using our model simply requires entering some basic information, such as the values of internal or external factors and the decision-maker's characteristics. Our model also defines the importance of each criterion in terms of what is known about the decision maker, the quality index and the utility of each alternative.

scholarly journals Online Probabilistic Goal Recognition over Nominal Models

2019 ◽  
Author(s):  
Ramon Fraga Pereira ◽  
Mor Vered ◽  
Felipe Meneguzzi ◽  
Miquel Ramírez
Keyword(s):  
Expert Knowledge ◽  
Error Rates ◽  
Prediction Errors ◽  
Model Learning ◽  
Dynamic Constraints ◽  
Posterior Probability Distribution ◽  
Model Based ◽  
Finite Set ◽  
Domain Models ◽  
Goal Recognition

This paper revisits probabilistic, model-based goal recognition to study the implications of the use of nominal models to estimate the posterior probability distribution over a finite set of hypothetical goals. Existing model-based approaches rely on expert knowledge to produce symbolic descriptions of the dynamic constraints domain objects are subject to, and these are assumed to produce correct predictions. We abandon this assumption to consider the use of nominal models that are learnt from observations on transitions of systems with unknown dynamics. Leveraging existing work on the acquisition of domain models via learning for Hybrid Planning we adapt and evaluate existing goal recognition approaches to analyze how prediction errors, inherent to system dynamics identification and model learning techniques have an impact over recognition error rates.

Application of a model-based optimisation methodology for nutrient removing SBRs leads to falsification of the model

2006 ◽  
Vol 53 (4-5) ◽  
pp. 95-103 ◽  
Author(s):  
G. Sin ◽  
K. Villez ◽  
P.A. Vanrolleghem
Keyword(s):  
Expert Knowledge ◽  
Pilot Scale ◽  
Optimal Operation ◽  
Mechanistic Models ◽  
Model Based ◽  
Improve Model ◽  
Better Than ◽  
Activated Sludge Systems ◽  
P Removal

Recently, a model-based optimisation methodology for SBR operation has been developed and an optimal operation scenario proposed to improve N and P removal in a pilot-scale SBR. In this study, this optimal operation scenario was implemented and evaluated. The results of the implementation showed that the SBR performance was improved by approximately 50 and 40% for total nitrogen and phosphorous removal, respectively, which was better than predicted by the model. However, the long-term SBR performance was found to be unstable, particularly owing to settling problems developed after the implementation. When confronted with reality, the model used for the optimisation of the operation was found to be invalid. The model was unable to predict the nitrite build-up provoked by the optimal operation scenario. These results imply that changing the operation of an SBR system using a model may significantly change the behaviour of the system beyond the (unknown) application domain of the model. This is simply because the mechanistic models currently do not cover all the aspects of activated sludge systems, e.g. settling and adaptation of the microbial community. To further improve model-application practices, expert knowledge (not contained in the models) can be valuable and should be incorporated into model-based process optimisations.

Daily Life Activity Routine Discovery in Hemiparetic Rehabilitation Patients Using Topic Models

2015 ◽  
Vol 54 (03) ◽  
pp. 248-255 ◽  
Author(s):  
A. Derungs ◽  
C. Schuster-Amft ◽  
O. Amft ◽  
G. Tröster ◽  
J. Seiter
Keyword(s):  
Topic Model ◽  
Expert Knowledge ◽  
Daily Life ◽  
Topic Models ◽  
Sensor Data ◽  
Life Activity ◽  
Rule Based ◽  
Model Based ◽  
Daily Life Activity ◽  
Rule Based Approach

Summary Background: Monitoring natural behavior and activity routines of hemiparetic rehabilitation patients across the day can provide valuable progress information for therapists and patients and contribute to an optimized rehabilitation process. In particular, continuous patient monitoring could add type, frequency and duration of daily life activity routines and hence complement standard clinical scores that are assessed for particular tasks only. Machine learning methods have been applied to infer activity routines from sensor data. However, supervised methods require activity annotations to build recognition models and thus require extensive patient supervision. Discovery methods, including topic models could provide patient routine information and deal with variability in activity and movement performance across patients. Topic models have been used to discover characteristic activity routine patterns of healthy individuals using activity primitives recognized from supervised sensor data. Yet, the applicability of topic models for hemiparetic rehabilitation patients and techniques to derive activity primitives without supervision needs to be addressed. Objectives: We investigate, 1) whether a topic model-based activity routine discovery framework can infer activity routines of rehabilitation patients from wearable motion sensor data. 2) We compare the performance of our topic model-based activity routine discovery using rule-based and clustering-based activity vocabulary. Methods: We analyze the activity routine discovery in a dataset recorded with 11 hemiparetic rehabilitation patients during up to ten full recording days per individual in an ambulatory daycare rehabilitation center using wearable motion sensors attached to both wrists and the non-affected thigh. We introduce and compare rule-based and clustering-based activity vocabulary to process statistical and frequency acceleration features to activity words. Activity words were used for activity routine pattern discovery using topic models based on Latent Dirichlet Allocation. Discovered activity routine patterns were then mapped to six categorized activity routines. Results: Using the rule-based approach, activity routines could be discovered with an average accuracy of 76% across all patients. The rule-based approach outperformed clustering by 10% and showed less confusions for predicted activity routines. Conclusion: Topic models are suitable to discover daily life activity routines in hemiparetic rehabilitation patients without trained classifiers and activity annotations. Activity routines show characteristic patterns regarding activity primitives including body and extremity postures and movement. A patient-independent rule set can be derived. Including expert knowledge supports successful activity routine discovery over completely data-driven clustering.

scholarly journals Cost and feasibility of a barrier to halt the spread of invasive cane toads in arid Australia: incorporating expert knowledge into model-based decision-making

2016 ◽  
Vol 54 (1) ◽  
pp. 216-224 ◽  
Author(s):  
Darren Southwell ◽  
Reid Tingley ◽  
Michael Bode ◽  
Emily Nicholson ◽  
Ben L. Phillips
Keyword(s):  
Decision Making ◽  
Expert Knowledge ◽  
Model Based

scholarly journals Knowledge base for wind farm devices in the computer expert system

2020 ◽  
Vol 68 (4) ◽  
pp. 107-118
Author(s):  
Radosław Duer ◽  
Stanisław Duer ◽  
Lech Drawski
Keyword(s):  
Artificial Intelligence ◽  
Renewable Energy ◽  
Expert System ◽  
Knowledge Base ◽  
Wind Farm ◽  
Expert Knowledge ◽  
Diagnostic Information ◽  
Diagnostic Model ◽  
Technical Diagnostics ◽  
Model Based

The article presents the issue of determining diagnostic information for the needs of testing the condition of wind farm equipment. To this end, the essence of the structure of an intelligent expert system was presented and described. The structure of the tested object is shown in the form of a functional and diagnostic model. Based on the developed model of the examined object, diagnostic information was determined in the form of a set of basic elements and a set of diagnostic signals, which are later used in the construction of an expert knowledge base. The expert knowledge base is determined by sets of facts and rules applied. An important part of this article is description of the structure of the expert system and the expert knowledge base used in it. Keywords: wind farm, renewable energy, technical diagnostics, diagnostic inference, artificial intelligence

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