A New Approach to the Use of Edge Extremities for Model-based Object Tracking

A New Approach to Multiple Fault Diagnosis: A Combination of Diagnostic Matrices, Graphs, Algebraic and Rule-Based Models. The Case of Two-Layer ModelsThe diagnosis of multiple faults is significantly more difficult than singular fault diagnosis. However, in realistic industrial systems the possibility of simultaneous occurrence of multiple faults must be taken into account. This paper investigates some of the limitations of the diagnostic model based on the simple binary diagnostic matrix in the case of multiple faults. Several possible interpretations of the diagnostic matrix with rule-based systems are provided and analyzed. A proposal of an extension of the basic, single-level model based on diagnostic matrices to a two-level one, founded on causal analysis and incorporating an OR and an AND matrix is put forward. An approach to the diagnosis of multiple faults based on inconsistency analysis is outlined, and a refinement procedure using a qualitative model of dependencies among system variables is sketched out.

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Hierarchical MRF model for model-based multi-object tracking

Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205) ◽

10.1109/icip.2001.959034 ◽

2002 ◽

Cited By ~ 1

Author(s):

Yunqiang Chen ◽

T.S. Huang

Keyword(s):

Object Tracking ◽

Model Based

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Application of Model-Based Tests for Analysing the Consequences of Mine Fires

Multidisciplinary Aspects of Production Engineering ◽

10.2478/mape-2018-0097 ◽

2018 ◽

Vol 1 (1) ◽

pp. 767-774

Author(s):

Magdalena Tutak

Keyword(s):

Fire Hazard ◽

Physical Parameters ◽

Gas Mixture ◽

New Approach ◽

Model Based ◽

Underground Coal Mines ◽

Mine Fires ◽

Underground Mine Workings ◽

The Subject ◽

Chemical Products

Abstract One of the most common and most dangerous hazards in underground coal mines is fire hazard. Mine fires can be exogenous or endogenous in nature. In the case of the former, a particular hazard is posed by methane fires that occur in dog headings and longwalls. Endogenous and exogenous fires are large hazard for working crew in mining headings and cause economics losses for mining plants. Mine fires result in emission of harmful chemical products and have a crucial impact on the physical parameters of the airflow. The subject of the article concerns the analysis of the consequences of methane fires in dog headings. These consequences were identified by means of model-based tests. For this purpose, a model was developed and boundary conditions were adopted to reflect the actual layout of the headings and the condition of the atmosphere in the area under analysis. The objective of the test was to determine the effects of methane fires on the chemical composition of the atmosphere and the physical parameters of the gas mixture generated in the process. The results obtained clearly indicate that fires have a significant impact on the above-mentioned values. The paper presents the distributions for the physical parameters of the resulting gas mixture and the concentration of fire gases. Moreover, it shows the distributions of temperature and oxygen concentration levels in the headings under analysis. The methodology developed for the application of model-based tests to analyse fire events in mine headings represents a new approach to the problem of investigating the consequences of such fires. It is also suitable for variant analyses of the processes related to the ventilation of underground mine workings as well as for analyses of emergency states. Model-based tests should support the assessment of the methane hazard levels and, subsequently, lead to an improvement of work safety in mines.

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A Robust Method for 3D Model-based Object Tracking

Research in Computer and Robot Vision ◽

10.1142/9789812812483_0018 ◽

1995 ◽

pp. 313-328

Author(s):

Masanobu Yamamoto

Keyword(s):

Object Tracking ◽

3D Model ◽

Robust Method ◽

Model Based

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Multi-cue 3D Model-Based Object Tracking

Visual Perception and Robotic Manipulation - Springer Tracts in Advanced Robotics ◽

10.1007/978-3-540-33455-2_5 ◽

2006 ◽

pp. 85-113

Keyword(s):

Object Tracking ◽

3D Model ◽

Model Based

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Improving Model-Based Genetic Programming for Symbolic Regression of Small Expressions

Evolutionary Computation ◽

10.1162/evco_a_00278 ◽

2020 ◽

pp. 1-27 ◽

Cited By ~ 1

Author(s):

M. Virgolin ◽

T. Alderliesten ◽

C. Witteveen ◽

P. A. N. Bosman

Keyword(s):

Genetic Programming ◽

Real World ◽

Symbolic Regression ◽

New Approach ◽

Model Based ◽

Real World Datasets ◽

Linkage Learning ◽

Optimal Mixing ◽

Crucial Parameter

The Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA) is a model-based EA framework that has been shown to perform well in several domains, including Genetic Programming (GP). Differently from traditional EAs where variation acts blindly, GOMEA learns a model of interdependencies within the genotype, that is, the linkage, to estimate what patterns to propagate. In this article, we study the role of Linkage Learning (LL) performed by GOMEA in Symbolic Regression (SR). We show that the non-uniformity in the distribution of the genotype in GP populations negatively biases LL, and propose a method to correct for this. We also propose approaches to improve LL when ephemeral random constants are used. Furthermore, we adapt a scheme of interleaving runs to alleviate the burden of tuning the population size, a crucial parameter for LL, to SR. We run experiments on 10 real-world datasets, enforcing a strict limitation on solution size, to enable interpretability. We find that the new LL method outperforms the standard one, and that GOMEA outperforms both traditional and semantic GP. We also find that the small solutions evolved by GOMEA are competitive with tuned decision trees, making GOMEA a promising new approach to SR.

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