The Experts below are selected from a list of 30942 Experts worldwide ranked by ideXlab platform
Subhrajit Sinha - One of the best experts on this subject based on the ideXlab platform.
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On Data-Driven Computation of Information Transfer for Causal Inference in Discrete-Time Dynamical Systems
Journal of Nonlinear Science, 2020Co-Authors: Subhrajit Sinha, Umesh VaidyaAbstract:In this paper, we provide a novel approach to capture Causal Interaction in a dynamical system from time series data. In Sinha and Vaidya (in: IEEE conference on decision and control, pp 7329–7334, 2016), we have shown that the existing measures of information transfer, namely directed information, Granger Causality and transfer entropy, fail to capture the Causal Interaction in a dynamical system and proposed a new definition of information transfer that captures direct Causal Interactions. The novelty of the information transfer definition used in this paper is the fact that it can differentiate between direct and indirect influences Sinha and Vaidya (2016). The main contribution of this paper is to show that the proposed definition of information transfers in Sinha and Vaidya (2016) and Sinha and Vaidya (in: Indian control conference, pp 303–308, 2017) can be computed from time series data, and thus, the direct influences in a dynamical system can be identified from time series data. We use transfer operator theoretic framework, involving Perron–Frobenius and Koopman operators for the data-driven approximation of the system dynamics and computation of information transfer. Several examples, involving linear and nonlinear system dynamics, are presented to verify the efficiency of the developed algorithm.
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On Data-Driven Computation of Information Transfer for Causal Inference in Dynamical Systems
arXiv: Optimization and Control, 2018Co-Authors: Subhrajit Sinha, Umesh VaidyaAbstract:In this paper, we provide a novel approach to capture Causal Interaction in a dynamical system from time-series data. In \cite{sinha_IT_CDC2016}, we have shown that the existing measures of information transfer, namely directed information, granger Causality and transfer entropy fail to capture true Causal Interaction in dynamical system and proposed a new definition of information transfer that captures true Causal Interaction. The main contribution of this paper is to show that the proposed definition of information transfer in \cite{sinha_IT_CDC2016}\cite{sinha_IT_ICC} can be computed from time-series data. We use transfer operator theoretic framework involving Perron-Frobenius and Koopman operators for the data-driven approximation of the system dynamics and for the computation of information transfer. Several examples involving linear and nonlinear system dynamics are presented to verify the efficiency of the developed algorithm.
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ACC - Data-Driven Approach for Inferencing Causality and Network Topology
2018 Annual American Control Conference (ACC), 2018Co-Authors: Subhrajit Sinha, Umesh VaidyaAbstract:In this paper, we provide a novel approach to capture Causal Interaction in a linear dynamical system from time-series data. In [1], we have shown that the existing measures of information transfer, namely directed information, Granger Causality and transfer entropy fail to capture true Causal Interaction in a dynamical system and proposed a new definition of information transfer that captures true Causal Interaction. The main contribution of this paper is to show that the proposed definition of information transfer in [1] [2] can be computed from time-series data and the computed information measure allows the identification of Causal Interaction and network topology in a dynamical system. The data-driven algorithm for computation of information transfer for linear systems relies on a robust optimization formulation of transfer operator theoretic framework. The proposed technique is applied to a number of different examples to establish its efficiency.
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identifying Causal Interaction in power system information based approach
Conference on Decision and Control, 2017Co-Authors: Subhrajit Sinha, Umesh Vaidya, Pranav Sharma, Venkataramana AjjarapuAbstract:Stability analysis of power system is a problem of immense importance in power community. Identification of the cause for instability is a relevant problem and has been studied widely. In this work we provide a novel approach, using the concept of information transfer in a dynamical system, to identify the states and the generators which are most responsible for instability in a given power network. Our developed notion of information transfer is physically motivated and has been previously shown to capture the true notion of Causality and influence. In this paper, we use information transfer measure to characterize Causal Interactions and influence in a power network. In particular, we identify the dynamic states of the generators (and the generator) which are most responsible for the instability. We further determine the states which contribute most to system oscillations and these findings reflect the physical intuitions of power network. Moreover, the analysis of information transfer from generators to load identifies which generators are most responsible for load fluctuations.
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CDC - Identifying Causal Interaction in power system: Information-based approach
2017 IEEE 56th Annual Conference on Decision and Control (CDC), 2017Co-Authors: Subhrajit Sinha, Umesh Vaidya, Pranav Sharma, Venkataramana AjjarapuAbstract:Stability analysis of power system is a problem of immense importance in power community. Identification of the cause for instability is a relevant problem and has been studied widely. In this work we provide a novel approach, using the concept of information transfer in a dynamical system, to identify the states and the generators which are most responsible for instability in a given power network. Our developed notion of information transfer is physically motivated and has been previously shown to capture the true notion of Causality and influence. In this paper, we use information transfer measure to characterize Causal Interactions and influence in a power network. In particular, we identify the dynamic states of the generators (and the generator) which are most responsible for the instability. We further determine the states which contribute most to system oscillations and these findings reflect the physical intuitions of power network. Moreover, the analysis of information transfer from generators to load identifies which generators are most responsible for load fluctuations.
Umesh Vaidya - One of the best experts on this subject based on the ideXlab platform.
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On Data-Driven Computation of Information Transfer for Causal Inference in Discrete-Time Dynamical Systems
Journal of Nonlinear Science, 2020Co-Authors: Subhrajit Sinha, Umesh VaidyaAbstract:In this paper, we provide a novel approach to capture Causal Interaction in a dynamical system from time series data. In Sinha and Vaidya (in: IEEE conference on decision and control, pp 7329–7334, 2016), we have shown that the existing measures of information transfer, namely directed information, Granger Causality and transfer entropy, fail to capture the Causal Interaction in a dynamical system and proposed a new definition of information transfer that captures direct Causal Interactions. The novelty of the information transfer definition used in this paper is the fact that it can differentiate between direct and indirect influences Sinha and Vaidya (2016). The main contribution of this paper is to show that the proposed definition of information transfers in Sinha and Vaidya (2016) and Sinha and Vaidya (in: Indian control conference, pp 303–308, 2017) can be computed from time series data, and thus, the direct influences in a dynamical system can be identified from time series data. We use transfer operator theoretic framework, involving Perron–Frobenius and Koopman operators for the data-driven approximation of the system dynamics and computation of information transfer. Several examples, involving linear and nonlinear system dynamics, are presented to verify the efficiency of the developed algorithm.
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On Data-Driven Computation of Information Transfer for Causal Inference in Dynamical Systems
arXiv: Optimization and Control, 2018Co-Authors: Subhrajit Sinha, Umesh VaidyaAbstract:In this paper, we provide a novel approach to capture Causal Interaction in a dynamical system from time-series data. In \cite{sinha_IT_CDC2016}, we have shown that the existing measures of information transfer, namely directed information, granger Causality and transfer entropy fail to capture true Causal Interaction in dynamical system and proposed a new definition of information transfer that captures true Causal Interaction. The main contribution of this paper is to show that the proposed definition of information transfer in \cite{sinha_IT_CDC2016}\cite{sinha_IT_ICC} can be computed from time-series data. We use transfer operator theoretic framework involving Perron-Frobenius and Koopman operators for the data-driven approximation of the system dynamics and for the computation of information transfer. Several examples involving linear and nonlinear system dynamics are presented to verify the efficiency of the developed algorithm.
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ACC - Data-Driven Approach for Inferencing Causality and Network Topology
2018 Annual American Control Conference (ACC), 2018Co-Authors: Subhrajit Sinha, Umesh VaidyaAbstract:In this paper, we provide a novel approach to capture Causal Interaction in a linear dynamical system from time-series data. In [1], we have shown that the existing measures of information transfer, namely directed information, Granger Causality and transfer entropy fail to capture true Causal Interaction in a dynamical system and proposed a new definition of information transfer that captures true Causal Interaction. The main contribution of this paper is to show that the proposed definition of information transfer in [1] [2] can be computed from time-series data and the computed information measure allows the identification of Causal Interaction and network topology in a dynamical system. The data-driven algorithm for computation of information transfer for linear systems relies on a robust optimization formulation of transfer operator theoretic framework. The proposed technique is applied to a number of different examples to establish its efficiency.
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identifying Causal Interaction in power system information based approach
Conference on Decision and Control, 2017Co-Authors: Subhrajit Sinha, Umesh Vaidya, Pranav Sharma, Venkataramana AjjarapuAbstract:Stability analysis of power system is a problem of immense importance in power community. Identification of the cause for instability is a relevant problem and has been studied widely. In this work we provide a novel approach, using the concept of information transfer in a dynamical system, to identify the states and the generators which are most responsible for instability in a given power network. Our developed notion of information transfer is physically motivated and has been previously shown to capture the true notion of Causality and influence. In this paper, we use information transfer measure to characterize Causal Interactions and influence in a power network. In particular, we identify the dynamic states of the generators (and the generator) which are most responsible for the instability. We further determine the states which contribute most to system oscillations and these findings reflect the physical intuitions of power network. Moreover, the analysis of information transfer from generators to load identifies which generators are most responsible for load fluctuations.
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CDC - Identifying Causal Interaction in power system: Information-based approach
2017 IEEE 56th Annual Conference on Decision and Control (CDC), 2017Co-Authors: Subhrajit Sinha, Umesh Vaidya, Pranav Sharma, Venkataramana AjjarapuAbstract:Stability analysis of power system is a problem of immense importance in power community. Identification of the cause for instability is a relevant problem and has been studied widely. In this work we provide a novel approach, using the concept of information transfer in a dynamical system, to identify the states and the generators which are most responsible for instability in a given power network. Our developed notion of information transfer is physically motivated and has been previously shown to capture the true notion of Causality and influence. In this paper, we use information transfer measure to characterize Causal Interactions and influence in a power network. In particular, we identify the dynamic states of the generators (and the generator) which are most responsible for the instability. We further determine the states which contribute most to system oscillations and these findings reflect the physical intuitions of power network. Moreover, the analysis of information transfer from generators to load identifies which generators are most responsible for load fluctuations.
Venkataramana Ajjarapu - One of the best experts on this subject based on the ideXlab platform.
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identifying Causal Interaction in power system information based approach
Conference on Decision and Control, 2017Co-Authors: Subhrajit Sinha, Umesh Vaidya, Pranav Sharma, Venkataramana AjjarapuAbstract:Stability analysis of power system is a problem of immense importance in power community. Identification of the cause for instability is a relevant problem and has been studied widely. In this work we provide a novel approach, using the concept of information transfer in a dynamical system, to identify the states and the generators which are most responsible for instability in a given power network. Our developed notion of information transfer is physically motivated and has been previously shown to capture the true notion of Causality and influence. In this paper, we use information transfer measure to characterize Causal Interactions and influence in a power network. In particular, we identify the dynamic states of the generators (and the generator) which are most responsible for the instability. We further determine the states which contribute most to system oscillations and these findings reflect the physical intuitions of power network. Moreover, the analysis of information transfer from generators to load identifies which generators are most responsible for load fluctuations.
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CDC - Identifying Causal Interaction in power system: Information-based approach
2017 IEEE 56th Annual Conference on Decision and Control (CDC), 2017Co-Authors: Subhrajit Sinha, Umesh Vaidya, Pranav Sharma, Venkataramana AjjarapuAbstract:Stability analysis of power system is a problem of immense importance in power community. Identification of the cause for instability is a relevant problem and has been studied widely. In this work we provide a novel approach, using the concept of information transfer in a dynamical system, to identify the states and the generators which are most responsible for instability in a given power network. Our developed notion of information transfer is physically motivated and has been previously shown to capture the true notion of Causality and influence. In this paper, we use information transfer measure to characterize Causal Interactions and influence in a power network. In particular, we identify the dynamic states of the generators (and the generator) which are most responsible for the instability. We further determine the states which contribute most to system oscillations and these findings reflect the physical intuitions of power network. Moreover, the analysis of information transfer from generators to load identifies which generators are most responsible for load fluctuations.
Yang Xiang - One of the best experts on this subject based on the ideXlab platform.
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direct Causal structure extraction from pairwise Interaction patterns in nat modeling bayesian networks
International Journal of Approximate Reasoning, 2019Co-Authors: Yang XiangAbstract:Abstract Non-impeding noisy-And Trees (NATs) provide a general, expressive, and efficient Causal model for conditional probability tables (CPTs) in discrete Bayesian networks (BNs). A CPT may be directly expressed as a NAT model or compressed into a NAT model. Once CPTs are NAT-modeled, efficiency of BN inference (both space and time) can be significantly improved. One of the critical operations in NAT modeling CPTs is extracting NAT structures from Interaction patterns between causes. The existing method does so through NAT databases coupled with search trees. Although the databases and search trees are compiled offline, the computation is costly and the dependency of NAT extraction on them adds a resource requirement for online computation. We present a novel method for direct NAT structure extraction from full and valid Causal Interaction patterns, based on bipartitions of causes. We then extend the method to NAT extraction from partial and invalid Interaction patterns. The resultant algorithm suite enables direct NAT extraction from all conceivable practical scenarios, with significantly reduced computational complexity, while eliminating dependency on NAT databases and search trees.
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fault tolerant direct nat structure extraction from pairwise Causal Interaction patterns
Scalable Uncertainty Management, 2017Co-Authors: Yang XiangAbstract:Non-impeding noisy-And Trees (NATs) provide a general, expressive, and efficient Causal model for conditional probability tables (CPTs) in discrete Bayesian networks (BNs). A CPT may be directly expressed as a NAT model or compressed into a NAT model. Once CPTs are NAT-modeled, efficiency of BN inference (both space and time) can be significantly improved. The most important operation in NAT modeling CPTs is extracting NAT structures from Interaction patterns between causes. Early method does so through a search tree coupled with a NAT database. A recent advance allows extraction of NAT structures from full, valid Causal Interaction patterns based on bipartition of causes, without requiring the search tree and the NAT database. In this work, we extend the method to direct NAT structure extraction from partial and invalid Causal Interaction patterns. This contribution enables direct NAT extraction from all conceivable application scenarios.
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SUM - Fault Tolerant Direct NAT Structure Extraction from Pairwise Causal Interaction Patterns
Lecture Notes in Computer Science, 2017Co-Authors: Yang XiangAbstract:Non-impeding noisy-And Trees (NATs) provide a general, expressive, and efficient Causal model for conditional probability tables (CPTs) in discrete Bayesian networks (BNs). A CPT may be directly expressed as a NAT model or compressed into a NAT model. Once CPTs are NAT-modeled, efficiency of BN inference (both space and time) can be significantly improved. The most important operation in NAT modeling CPTs is extracting NAT structures from Interaction patterns between causes. Early method does so through a search tree coupled with a NAT database. A recent advance allows extraction of NAT structures from full, valid Causal Interaction patterns based on bipartition of causes, without requiring the search tree and the NAT database. In this work, we extend the method to direct NAT structure extraction from partial and invalid Causal Interaction patterns. This contribution enables direct NAT extraction from all conceivable application scenarios.
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Acquisition of Causal models for local distributions in Bayesian networks.
IEEE Transactions on Cybernetics, 2014Co-Authors: Yang Xiang, Minh Tri TruongAbstract:To specify a Bayesian network, a local distribution in the form of a conditional probability table, often of an effect conditioned on its n causes, needs to be acquired, one for each non-root node. Since the number of parameters to be assessed is generally exponential in n , improving the efficiency is an important concern in knowledge engineering. Non-impeding noisy-AND (NIN-AND) tree Causal models reduce the number of parameters to being linear in n , while explicitly expressing both reinforcing and undermining Interactions among causes. The key challenge in NIN-AND tree modeling is the acquisition of the NIN-AND tree structure. In this paper, we formulate a concise structure representation and an expressive Causal Interaction function of NIN-AND trees. Building on these representations, we propose two structural acquisition methods, which are applicable to both elicitation-based and machine learning-based acquisitions. Their accuracy is demonstrated through experimental evaluations.
Lei Ding - One of the best experts on this subject based on the ideXlab platform.
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ictal source analysis localization and imaging of Causal Interactions in humans
NeuroImage, 2007Co-Authors: Lei Ding, Gregory A Worrell, Terrence D Lagerlund, Bin HeAbstract:Abstract We propose a new integrative approach to characterize the structure of seizures in the space, time, and frequency domains. Such characterization leads to a new technical development of ictal source analysis for the presurgical evaluation of epilepsy patients. The present new ictal source analysis method consists of three parts. First, a three-dimensional source scanning procedure is performed by a spatio-temporal FINE source localization method to locate the multiple sources responsible for the time evolving ictal rhythms at their onsets. Next, the dynamic behavior of the sources is modeled by a multivariate autoregressive process (MVAR). Lastly, the Causal Interaction patterns among the sources as a function of frequency are estimated from the MVAR modeling of the source temporal dynamics. The Causal Interaction patterns indicate the dynamic communications between sources, which are useful in distinguishing the primary sources responsible for the ictal onset from the secondary sources caused by the ictal propagation. The present ictal analysis strategy has been applied to a number of seizures from five epilepsy patients, and their results are consistent with observations from either MRI lesions or SPECT scans, which indicate its effectiveness. Each step of the ictal source analysis is statistically evaluated in order to guarantee the confidence in the results.
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An Adaptive Directed Transfer Function Approach for Detecting Dynamic Causal Interactions
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Inte, 2007Co-Authors: Christopher Wilke, Lei DingAbstract:This paper proposes the application of the directed transfer function (DTF) to a set of time-varying coefficients obtained through the use of a multivariate adaptive autoregressive (MVAAR) model. We define this time-varying measure of Causality as the adaptive directed transfer function (ADTF) and compare its ability to discern changes in the Causal Interaction pattern as compared with the conventional DTF. To accomplish this task, two multivariate models with predefined Interaction patterns were created in which the Causal Interaction between the nodes was altered during the course of the time series. In both models, the ADTF has the capability to discern the dynamic changes in the primary source of the information outflow. The results obtained by using the ADTF were subsequently compared to those calculated through use of the conventional DTF method and the present simulation result suggests that use of the ADTF could provide useful information regarding dynamic Causality.
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Ictal source analysis: localization and imaging of Causal Interactions in humans.
NeuroImage, 2006Co-Authors: Lei Ding, Gregory A Worrell, Terrence D LagerlundAbstract:We propose a new integrative approach to characterize the structure of seizures in the space, time, and frequency domains. Such characterization leads to a new technical development of ictal source analysis for the presurgical evaluation of epilepsy patients. The present new ictal source analysis method consists of three parts. First, a three-dimensional source scanning procedure is performed by a spatio-temporal FINE source localization method to locate the multiple sources responsible for the time evolving ictal rhythms at their onsets. Next, the dynamic behavior of the sources is modeled by a multivariate autoregressive process (MVAR). Lastly, the Causal Interaction patterns among the sources as a function of frequency are estimated from the MVAR modeling of the source temporal dynamics. The Causal Interaction patterns indicate the dynamic communications between sources, which are useful in distinguishing the primary sources responsible for the ictal onset from the secondary sources caused by the ictal propagation. The present ictal analysis strategy has been applied to a number of seizures from five epilepsy patients, and their results are consistent with observations from either MRI lesions or SPECT scans, which indicate its effectiveness. Each step of the ictal source analysis is statistically evaluated in order to guarantee the confidence in the results.