Functional State Estimation Methodology Based on Fuzzy Clustering for Complex Process Monitoring

2012 ◽  
pp. 340-349
Author(s):  
Henry Sarmiento ◽  
Claudia Isaza ◽  
Tatiana Kempowsky-Hamon
Keyword(s):  
State Estimation ◽  
Process Monitoring ◽  
Functional State ◽  
Fuzzy Clustering ◽  
Complex Process

Business Process Model Abstraction Based on Fuzzy Clustering Analysis

2019 ◽  
Vol 28 (03) ◽  
pp. 1950007
Author(s):  
Nan Wang ◽  
Shanwu Sun ◽  
Ying Liu ◽  
Senyue Zhang
Keyword(s):  
Business Process ◽  
Fuzzy Clustering ◽  
Process Model ◽  
Control Flow ◽  
Business Process Model ◽  
Model Abstraction ◽  
Fuzzy Matrix ◽  
Fuzzy Clustering Analysis ◽  
Complex Process ◽  
Hard Clustering

The most prominent Business Process Model Abstraction (BPMA) use case is a construction of a process “quick view” for rapidly comprehending a complex process. Researchers propose various process abstraction methods to aggregate the activities most of which are based on [Formula: see text]-means hard clustering. This paper focuses on the limitation of hard clustering, i.e. it cannot identify the special activities (called “edge activities” in this paper) and each activity must be classified to some subprocess. A new method is proposed to classify activities based on fuzzy clustering which generates a fuzzy matrix by computing the possibilities of activities belonging to subprocesses. According to this matrix, the “edge activities” can be located. Considering the structure correlation feature of the activities in subprocesses, an approach is provided to generate the initial clusters based on the close connection characteristics of subprocesses. A hard partition algorithm is proposed to classify the edge activities and it evaluates the generated abstract models according to a new index designed by control flow order preserving requirement and the evaluation results guide the edge activities to be classified to the optimal hard partition. The proposed method is applied to a process model repository in use. The results verify the validity of the measurement based on the virtual document to generating fuzzy matrix. Also it mines the threshold parameter in the real world process model collection enriched with human designed subprocesses to compute the fuzzy matrix. Furthermore, a comparison is made between the proposed method and the [Formula: see text]-means clustering and the results show our approach more closely approximating the decisions of the involved modelers to cluster activities and it contributes to the development of modeling support for effective process model abstraction.

The effect of geometry on tumor thermal profile and its use in tumor functional state estimation

2014 ◽  
Vol 8 (3) ◽  
pp. 258-264 ◽  
Author(s):  
Michal Tepper ◽  
Asaf Shoval ◽  
Israel Gannot
Keyword(s):  
State Estimation ◽  
Functional State ◽  
Thermal Profile

scholarly journals Classifying human operator functional state based on electrophysiological and performance measures and fuzzy clustering method

2013 ◽  
Vol 7 (6) ◽  
pp. 477-494 ◽  
Author(s):  
Jian-Hua Zhang ◽  
Xiao-Di Peng ◽  
Hua Liu ◽  
Jörg Raisch ◽  
Ru-Bin Wang
Keyword(s):  
Functional State ◽  
Performance Measures ◽  
Fuzzy Clustering ◽  
Human Operator ◽  
Clustering Method ◽  
Fuzzy Clustering Method ◽  
Operator Functional State ◽  
And Performance

Multi-model Approach to Human Functional State Estimation

2016 ◽  
pp. 188-197 ◽  
Author(s):  
Kevin Durkee ◽  
Avinash Hiriyanna ◽  
Scott Pappada ◽  
John Feeney ◽  
Scott Galster
Keyword(s):  
State Estimation ◽  
Functional State ◽  
Model Approach

Using Context to Optimize a Functional State Estimation Engine in Unmanned Aircraft System Operations

2015 ◽  
pp. 24-35 ◽  
Author(s):  
Kevin Durkee ◽  
Scott Pappada ◽  
Andres Ortiz ◽  
John Feeney ◽  
Scott Galster
Keyword(s):  
State Estimation ◽  
Functional State ◽  
Unmanned Aircraft ◽  
Unmanned Aircraft System ◽  
Aircraft System

Nonlinear stochastic modeling to improve state estimation in process monitoring and control

AIChE Journal ◽  
2010 ◽  
Vol 57 (4) ◽  
pp. 996-1007 ◽  
Author(s):  
Fernando V. Lima ◽  
James B. Rawlings
Keyword(s):  
State Estimation ◽  
Stochastic Modeling ◽  
Process Monitoring ◽  
Monitoring And Control ◽  
Process Monitoring And Control ◽  
And Control ◽  
Improve State

Adaptive Tracking Algorithm of Multi-Target Based on Fuzzy Clustering

2014 ◽  
Vol 513-517 ◽  
pp. 448-452
Author(s):  
Xiu Hua Hu ◽  
Lei Guo ◽  
Hui Hui Li
Keyword(s):  
State Estimation ◽  
Target Tracking ◽  
Fuzzy Clustering ◽  
Tracking System ◽  
Joint Probability ◽  
Tracking Algorithm ◽  
Adaptive Tracking ◽  
Correlation Criterion ◽  
Multi Target Tracking ◽  
Fuzzy Clustering Theory

For multi-target tracking system, aiming at solving the problem of low precision of state estimation caused by the data correlation ambiguity, the paper presents a novel multi-sensor multi-target adaptive tracking algorithm based on fuzzy clustering theory. Based on the joint probability data association algorithm, the new approach takes account of the case that whether the measure is validated and its possibility of belong to false alarm, and improves the correlation criterion of effective measurement with existing track on the basis of fuzzy clustering theory, which all perfect the update equation of target state estimation and the covariance. Meanwhile, with the adaptive distributed fusion processing structure, it enhance the robustness of the system and without prejudice to the real-time tracking. With the simulation case studies of radar/infrared sensor fusion multi-target tracking system, it verifies the effectiveness of the proposed approach.

New results in common functional state estimation for two linear systems with unknown inputs

2015 ◽  
Vol 13 (6) ◽  
pp. 1538-1543 ◽  
Author(s):  
Jiunn Yea Ng ◽  
Chee Pin Tan ◽  
Kok Yew Ng ◽  
Hieu Trinh
Keyword(s):  
State Estimation ◽  
Linear Systems ◽  
Functional State ◽  
Unknown Inputs
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