The Experts below are selected from a list of 189042 Experts worldwide ranked by ideXlab platform
Anup Basu - One of the best experts on this subject based on the ideXlab platform.
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anatomy preserving 3d Model Decomposition based on robust skeleton surface node correspondence
International Conference on Multimedia and Expo, 2011Co-Authors: Liang Shi, Irene Cheng, Anup BasuAbstract:In this work, we present an effective anatomy preserving Model Decomposition technique. By extracting unit-width curve skeletons, which are robust to noise, and mapping skeleton branches to Model surface nodes, our method accurately identifies the topology and geometry information of a 3D Model, resulting in more semantically rich segmented components. Experiments on 2194 Models from the Princeton Shape Benchmark and A Benchmark for 3D Mesh Segmentation demonstrate the advantage of the proposed technique. Our results preserve better anatomical structures compared to three commonly used Model segmentation methods.
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ICME - Anatomy preserving 3D Model Decomposition based on robust skeleton-surface node correspondence
2011 IEEE International Conference on Multimedia and Expo, 2011Co-Authors: Liang Shi, Irene Cheng, Anup BasuAbstract:In this work, we present an effective anatomy preserving Model Decomposition technique. By extracting unit-width curve skeletons, which are robust to noise, and mapping skeleton branches to Model surface nodes, our method accurately identifies the topology and geometry information of a 3D Model, resulting in more semantically rich segmented components. Experiments on 2194 Models from the Princeton Shape Benchmark and A Benchmark for 3D Mesh Segmentation demonstrate the advantage of the proposed technique. Our results preserve better anatomical structures compared to three commonly used Model segmentation methods.
Liang Shi - One of the best experts on this subject based on the ideXlab platform.
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anatomy preserving 3d Model Decomposition based on robust skeleton surface node correspondence
International Conference on Multimedia and Expo, 2011Co-Authors: Liang Shi, Irene Cheng, Anup BasuAbstract:In this work, we present an effective anatomy preserving Model Decomposition technique. By extracting unit-width curve skeletons, which are robust to noise, and mapping skeleton branches to Model surface nodes, our method accurately identifies the topology and geometry information of a 3D Model, resulting in more semantically rich segmented components. Experiments on 2194 Models from the Princeton Shape Benchmark and A Benchmark for 3D Mesh Segmentation demonstrate the advantage of the proposed technique. Our results preserve better anatomical structures compared to three commonly used Model segmentation methods.
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ICME - Anatomy preserving 3D Model Decomposition based on robust skeleton-surface node correspondence
2011 IEEE International Conference on Multimedia and Expo, 2011Co-Authors: Liang Shi, Irene Cheng, Anup BasuAbstract:In this work, we present an effective anatomy preserving Model Decomposition technique. By extracting unit-width curve skeletons, which are robust to noise, and mapping skeleton branches to Model surface nodes, our method accurately identifies the topology and geometry information of a 3D Model, resulting in more semantically rich segmented components. Experiments on 2194 Models from the Princeton Shape Benchmark and A Benchmark for 3D Mesh Segmentation demonstrate the advantage of the proposed technique. Our results preserve better anatomical structures compared to three commonly used Model segmentation methods.
Irene Cheng - One of the best experts on this subject based on the ideXlab platform.
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anatomy preserving 3d Model Decomposition based on robust skeleton surface node correspondence
International Conference on Multimedia and Expo, 2011Co-Authors: Liang Shi, Irene Cheng, Anup BasuAbstract:In this work, we present an effective anatomy preserving Model Decomposition technique. By extracting unit-width curve skeletons, which are robust to noise, and mapping skeleton branches to Model surface nodes, our method accurately identifies the topology and geometry information of a 3D Model, resulting in more semantically rich segmented components. Experiments on 2194 Models from the Princeton Shape Benchmark and A Benchmark for 3D Mesh Segmentation demonstrate the advantage of the proposed technique. Our results preserve better anatomical structures compared to three commonly used Model segmentation methods.
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ICME - Anatomy preserving 3D Model Decomposition based on robust skeleton-surface node correspondence
2011 IEEE International Conference on Multimedia and Expo, 2011Co-Authors: Liang Shi, Irene Cheng, Anup BasuAbstract:In this work, we present an effective anatomy preserving Model Decomposition technique. By extracting unit-width curve skeletons, which are robust to noise, and mapping skeleton branches to Model surface nodes, our method accurately identifies the topology and geometry information of a 3D Model, resulting in more semantically rich segmented components. Experiments on 2194 Models from the Princeton Shape Benchmark and A Benchmark for 3D Mesh Segmentation demonstrate the advantage of the proposed technique. Our results preserve better anatomical structures compared to three commonly used Model segmentation methods.
Anibal Bregon - One of the best experts on this subject based on the ideXlab platform.
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State space neural networks and Model-Decomposition methods for fault diagnosis of complex industrial systems
Engineering Applications of Artificial Intelligence, 2019Co-Authors: Belarmino Pulido, Jesús M. Zamarreño, A. Merino, Anibal BregonAbstract:Abstract Reliable and timely fault detection and isolation are necessary tasks to guarantee continuous performance in complex industrial systems, avoiding failure propagation in the system and helping to minimize downtime. Model-based diagnosis fulfils those requirements, and has the additional advantage of using reusable Models. However, reusing existing complex non-linear Models for diagnosis in large industrial systems is not straightforward. Most of the times, the Models have been created for other purposes different from diagnosis, and many times the required analytical redundancy is small. The approach proposed in this work combines techniques from two different research communities within Artificial Intelligence: Model-based Reasoning and Neural Networks. In particular, in this work we propose to use Possible Conflicts, which is a Model Decomposition technique from the Artificial Intelligence community to provide the structure (equations, inputs, outputs, and state variables) of minimal Models able to perform fault detection and isolation. Such structural information is then used to design a grey box Model by means of state space neural networks. In this work we prove that the structure of the Minimal Evaluable Model for a Possible Conflict can be used in real-world industrial systems to guide the design of the state space Model of the neural network, reducing its complexity and avoiding the process of multiple unknown parameter estimation in the first principles Models. We demonstrate the feasibility of the approach in an evaporator for a beet sugar factory using real data.
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Diagnosis of Hybrid Systems Using Structural Model Decomposition
Fault Diagnosis of Hybrid Dynamic and Complex Systems, 2018Co-Authors: Matthew Daigle, Anibal Bregon, Indranil RoychoudhuryAbstract:As engineering systems increase in complexity, it becomes more crucial to have automated fault diagnosis to ensure that system faults and failures can be quickly isolated and identified, and appropriate responses quickly executed. Complex systems are also increasingly hybrid, that is, they exhibit mixed discrete and continuous behavior. Faults in these system can manifest in both the continuous dynamics (e.g., as system parameter changes) and the discrete dynamics (e.g., as changes in component operational modes). In such systems, the complexity of fault diagnosis increases significantly. Due to the large number of possible system modes, and possible mode changes, including those that may occur during fault diagnosis, most available diagnosis approaches become prohibitively computationally expensive. This chapter develops a qualitative fault isolation framework for diagnosis of hybrid systems, based on the analysis of residual signals, and under bounded observation delay. Central to the approach is the concept of structural Model Decomposition, which essentially defines several smaller independent diagnosis problems that become more efficient to solve than the global system-level diagnosis problem. As a result, the developed methodology is efficient and scalable. The approach is applied to an electrical power system testbed, and simulation results demonstrate the efficacy of the approach and the computational advantages provided by structural Model Decomposition.
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Qualitative Fault Isolation of Hybrid Systems: A Structural Model Decomposition-Based Approach
2016Co-Authors: Anibal Bregon, Matthew Daigle, Indranil RoychoudhuryAbstract:Quick and robust fault diagnosis is critical to ensuring safe operation of complex engineering systems. A large number of techniques are available to provide fault diagnosis in systems with continuous dynamics. However, many systems in aerospace and industrial environments are best represented as hybrid systems that consist of discrete behavioral modes, each with its own continuous dynamics. These hybrid dynamics make the on-line fault diagnosis task computationally more complex due to the large number of possible system modes and the existence of autonomous mode transitions. This paper presents a qualitative fault isolation framework for hybrid systems based on structural Model Decomposition. The fault isolation is performed by analyzing the qualitative information of the residual deviations. However, in hybrid systems this process becomes complex due to possible existence of observation delays, which can cause observed deviations to be inconsistent with the expected deviations for the current mode in the system. The great advantage of structural Model Decomposition is that (i) it allows to design residuals that respond to only a subset of the faults, and (ii) every time a mode change occurs, only a subset of the residuals will need to be reconfigured, thus reducing the complexity of the reasoning process for isolation purposes. To demonstrate and test the validity of our approach, we use an electric circuit simulation as the case study.
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A qualitative event-based approach to multiple fault diagnosis in continuous systems using structural Model Decomposition
Engineering Applications of Artificial Intelligence, 2016Co-Authors: Matthew Daigle, Anibal Bregon, Gautam Biswas, Xenofon Koutsoukos, Belarmino PulidoAbstract:Multiple fault diagnosis is a difficult problem for dynamic systems, and, as a result, most multiple fault diagnosis approaches are restricted to static systems, and most dynamic system diagnosis approaches make the single fault assumption. Within the framework of consistency-based diagnosis, the challenge is to generate conflicts from dynamic signals. For multiple faults, this becomes difficult due to the possibility of fault masking and different relative times of fault occurrence, resulting in many different ways that any given combination of faults can manifest in the observations. In order to address these challenges, we develop a novel multiple fault diagnosis framework for continuous dynamic systems. We construct a qualitative event-based framework, in which discrete qualitative symbols are generated from residual signals. Within this framework, we formulate an online diagnosis approach and establish definitions of multiple fault diagnosability. Residual generators are constructed based on structural Model Decomposition, which, as we demonstrate, has the effect of reducing the impact of fault masking by decoupling faults from residuals, thus improving diagnosability and fault isolation performance. Through simulation-based multiple fault diagnosis experiments, we demonstrate and validate the concepts developed here, using a multi-tank system as a case study.
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An event-based distributed diagnosis framework using structural Model Decomposition
Artificial Intelligence, 2014Co-Authors: Anibal Bregon, Indranil Roychoudhury, Matthew Daigle, Gautam Biswas, Xenofon Koutsoukos, Belarmino PulidoAbstract:Complex engineering systems require efficient on-line fault diagnosis methodologies to improve safety and reduce maintenance costs. Traditionally, diagnosis approaches are centralized, but these solutions do not scale well. Also, centralized diagnosis solutions are difficult to implement on increasingly prevalent distributed, networked embedded systems. This paper presents a distributed diagnosis framework for physical systems with continuous behavior. Using Possible Conflicts, a structural Model Decomposition method from the Artificial Intelligence Model-based diagnosis (DX) community, we develop a distributed diagnoser design algorithm to build local event-based diagnosers. These diagnosers are constructed based on global diagnosability analysis of the system, enabling them to generate local diagnosis results that are globally correct without the use of a centralized coordinator. We also use Possible Conflicts to design local parameter estimators that are integrated with the local diagnosers to form a comprehensive distributed diagnosis framework. Hence, this is a fully distributed approach to fault detection, isolation, and identification. We evaluate the developed scheme on a four-wheeled rover for different design scenarios to show the advantages of using Possible Conflicts, and generate on-line diagnosis results in simulation to demonstrate the approach.
Indranil Roychoudhury - One of the best experts on this subject based on the ideXlab platform.
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Diagnosis of Hybrid Systems Using Structural Model Decomposition
Fault Diagnosis of Hybrid Dynamic and Complex Systems, 2018Co-Authors: Matthew Daigle, Anibal Bregon, Indranil RoychoudhuryAbstract:As engineering systems increase in complexity, it becomes more crucial to have automated fault diagnosis to ensure that system faults and failures can be quickly isolated and identified, and appropriate responses quickly executed. Complex systems are also increasingly hybrid, that is, they exhibit mixed discrete and continuous behavior. Faults in these system can manifest in both the continuous dynamics (e.g., as system parameter changes) and the discrete dynamics (e.g., as changes in component operational modes). In such systems, the complexity of fault diagnosis increases significantly. Due to the large number of possible system modes, and possible mode changes, including those that may occur during fault diagnosis, most available diagnosis approaches become prohibitively computationally expensive. This chapter develops a qualitative fault isolation framework for diagnosis of hybrid systems, based on the analysis of residual signals, and under bounded observation delay. Central to the approach is the concept of structural Model Decomposition, which essentially defines several smaller independent diagnosis problems that become more efficient to solve than the global system-level diagnosis problem. As a result, the developed methodology is efficient and scalable. The approach is applied to an electrical power system testbed, and simulation results demonstrate the efficacy of the approach and the computational advantages provided by structural Model Decomposition.
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Qualitative Fault Isolation of Hybrid Systems: A Structural Model Decomposition-Based Approach
2016Co-Authors: Anibal Bregon, Matthew Daigle, Indranil RoychoudhuryAbstract:Quick and robust fault diagnosis is critical to ensuring safe operation of complex engineering systems. A large number of techniques are available to provide fault diagnosis in systems with continuous dynamics. However, many systems in aerospace and industrial environments are best represented as hybrid systems that consist of discrete behavioral modes, each with its own continuous dynamics. These hybrid dynamics make the on-line fault diagnosis task computationally more complex due to the large number of possible system modes and the existence of autonomous mode transitions. This paper presents a qualitative fault isolation framework for hybrid systems based on structural Model Decomposition. The fault isolation is performed by analyzing the qualitative information of the residual deviations. However, in hybrid systems this process becomes complex due to possible existence of observation delays, which can cause observed deviations to be inconsistent with the expected deviations for the current mode in the system. The great advantage of structural Model Decomposition is that (i) it allows to design residuals that respond to only a subset of the faults, and (ii) every time a mode change occurs, only a subset of the residuals will need to be reconfigured, thus reducing the complexity of the reasoning process for isolation purposes. To demonstrate and test the validity of our approach, we use an electric circuit simulation as the case study.
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An event-based distributed diagnosis framework using structural Model Decomposition
Artificial Intelligence, 2014Co-Authors: Anibal Bregon, Indranil Roychoudhury, Matthew Daigle, Gautam Biswas, Xenofon Koutsoukos, Belarmino PulidoAbstract:Complex engineering systems require efficient on-line fault diagnosis methodologies to improve safety and reduce maintenance costs. Traditionally, diagnosis approaches are centralized, but these solutions do not scale well. Also, centralized diagnosis solutions are difficult to implement on increasingly prevalent distributed, networked embedded systems. This paper presents a distributed diagnosis framework for physical systems with continuous behavior. Using Possible Conflicts, a structural Model Decomposition method from the Artificial Intelligence Model-based diagnosis (DX) community, we develop a distributed diagnoser design algorithm to build local event-based diagnosers. These diagnosers are constructed based on global diagnosability analysis of the system, enabling them to generate local diagnosis results that are globally correct without the use of a centralized coordinator. We also use Possible Conflicts to design local parameter estimators that are integrated with the local diagnosers to form a comprehensive distributed diagnosis framework. Hence, this is a fully distributed approach to fault detection, isolation, and identification. We evaluate the developed scheme on a four-wheeled rover for different design scenarios to show the advantages of using Possible Conflicts, and generate on-line diagnosis results in simulation to demonstrate the approach.
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Distributed Prognostics Based on Structural Model Decomposition
IEEE Transactions on Reliability, 2014Co-Authors: Matthew Daigle, Anibal Bregon, Indranil RoychoudhuryAbstract:Within systems health management, prognostics focuses on predicting the remaining useful life of a system. In the Model-based prognostics paradigm, physics-based Models are constructed that describe the operation of a system, and how it fails. Such approaches consist of an estimation phase, in which the health state of the system is first identified, and a prediction phase, in which the health state is projected forward in time to determine the end of life. Centralized solutions to these problems are often computationally expensive, do not scale well as the size of the system grows, and introduce a single point of failure. In this paper, we propose a novel distributed Model-based prognostics scheme that formally describes how to decompose both the estimation and prediction problems into computationally-independent local subproblems whose solutions may be easily composed into a global solution. The Decomposition of the prognostics problem is achieved through structural Decomposition of the underlying Models. The Decomposition algorithm creates from the global system Model a set of local subModels suitable for prognostics. Computationally independent local estimation and prediction problems are formed based on these local subModels, resulting in a scalable distributed prognostics approach that allows the local subproblems to be solved in parallel, thus offering increases in computational efficiency. Using a centrifugal pump as a case study, we perform a number of simulation-based experiments to demonstrate the distributed approach, compare the performance with a centralized approach, and establish its scalability.
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An Efficient Model-based Diagnosis Engine for Hybrid Systems Using Structural Model Decomposition
2013Co-Authors: Anibal Bregon, Indranil Roychoudhury, Matthew Daigle, Sriram Narasimhan, Belarmino PulidoAbstract:Complex hybrid systems are present in a large range of engineering applications, like mechanical systems, electrical circuits, or embedded computation systems. The behavior of these systems is made up of continuous and discrete event dynamics that increase the difficulties for accurate and timely online fault diagnosis. The Hybrid Diagnosis Engine (HyDE) offers flexibility to the diagnosis application designer to choose the Modeling paradigm and the reasoning algorithms. The HyDE architecture supports the use of multiple Modeling paradigms at the component and system level. However, HyDE faces some problems regarding performance in terms of complexity and time. Our focus in this paper is on developing efficient Model-based methodologies for online fault diagnosis in complex hybrid systems. To do this, we propose a diagnosis framework where structural Model Decomposition is integrated within the HyDE diagnosis framework to reduce the computational complexity associated with the fault diagnosis of hybrid systems. As a case study, we apply our approach to a diagnostic testbed, the Advanced Diagnostics and Prognostics Testbed (ADAPT), using real data.