References of "Yuan, Ye"
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See detailNetwork Identifiability from Intrinsic Noise
Goncalves, Jorge UL; Hayden, David; Yuan, Ye

in IEEE Transactions on Automatic Control (in press)

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See detailA minimal realization technique for the dynamical structure function of a class of LTI systems
Goncalves, Jorge UL; Yuan, Ye; Rai, Anurag et al

in IEEE Transactions on Control of Network Systems (in press)

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See detailDynamical differential expression (DyDE) reveals the period control mechanisms of the Arabidopsis circadian oscillator
Mombaerts, Laurent UL; Carignano, Alberto; Robertson, Fiona et al

in PLoS Computational Biology (2019)

The circadian oscillator, an internal time-keeping device found in most organisms, enables timely regulation of daily biological activities by maintaining synchrony with the external environment. The ... [more ▼]

The circadian oscillator, an internal time-keeping device found in most organisms, enables timely regulation of daily biological activities by maintaining synchrony with the external environment. The mechanistic basis underlying the adjustment of circadian rhythms to changing external conditions, however, has yet to be clearly elucidated. We explored the mechanism of action of nicotinamide in Arabidopsis thaliana, a metabolite that lengthens the period of circadian rhythms, to understand the regulation of circadian period. To identify the key mechanisms involved in the circadian response to nicotinamide, we developed a systematic and practical modeling framework based on the identification and comparison of gene regulatory dynamics. Our mathematical predictions, confirmed by experimentation, identified key transcriptional regulatory mechanisms of circadian period and uncovered the role of blue light in the response of the circadian oscillator to nicotinamide. We suggest that our methodology could be adapted to predict mechanisms of drug action in complex biological systems. [less ▲]

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See detailRobust stability analysis of active voltage control for high-power IGBT switching by Kharitonov's theorem
Goncalves, Jorge UL; Yuan, Ye; Yang, Xin et al

in IEEE Transactions on Power Electronics (2016), 31(3), 2584-2595

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See detailA Sparse Bayesian Approach to the Identification of Nonlinear State-Space Systems
Pan, Wei UL; Yuan, Ye; Goncalves, Jorge UL et al

in IEEE Transaction on Automatic Control (2016), 61(1), 182-187

This technical note considers the identification of nonlinear discrete-time systems with additive process noise but without measurement noise. In particular, we propose a method and its associated ... [more ▼]

This technical note considers the identification of nonlinear discrete-time systems with additive process noise but without measurement noise. In particular, we propose a method and its associated algorithm to identify the system nonlinear functional forms and their associated parameters from a limited number of time-series data points. For this, we cast this identification problem as a sparse linear regression problem and take a Bayesian viewpoint to solve it. As such, this approach typically leads to nonconvex optimisations. We propose a convexification procedure relying on an efficient iterative re-weighted ℓ1-minimisation algorithm that uses general sparsity inducing priors on the parameters of the system and marginal likelihood maximisation. Using this approach, we also show how convex constraints on the parameters can be easily added to our proposed iterative re-weighted ℓ1-minimisation algorithm. In the supplementary material \cite{appendix}, we illustrate the effectiveness of the proposed identification method on two classical systems in biology and physics, namely, a genetic repressilator network and a large scale network of interconnected Kuramoto oscillators. [less ▲]

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See detailIdentifying Biochemical Reaction Networks From Heterogeneous Datasets
Goncalves, Jorge UL; Pan, Wei; Yuan, Ye et al

in IEEE Conference on Decision and Control, Osaka, Japan, December 2015 (2015, December)

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See detailOn minimal realisations of dynamical structure functions
Yuan, Ye; Glover, Keith; Goncalves, Jorge UL

in Automatica (2015), 55

Motivated by the fact that transfer functions do not contain structural information about networks, dynamical structure functions were introduced to capture causal relationships between measured nodes in ... [more ▼]

Motivated by the fact that transfer functions do not contain structural information about networks, dynamical structure functions were introduced to capture causal relationships between measured nodes in networks. From the dynamical structure functions, a) we show that the actual number of hidden states can be larger than the number of hidden states estimated from the corresponding transfer function; b) we can obtain partial information about the true state-space equation, which cannot in general be obtained from the transfer function. Based on these properties, this paper proposes algorithms to find minimal realisations for a given dynamical structure function. This helps to estimate the minimal number of hidden states, to better understand the complexity of the network, and to identify potential targets for new measurements. [less ▲]

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See detailOnline Fault Diagnosis for Nonlinear Power Systems
Pan, Wei UL; Yuan, Ye; Sandberg, Henrik et al

in Automatica (2015), 55

In this paper, automatic fault diagnosis in large scale power networks described by second-order nonlinear swing equations is studied. This work focuses on a class of faults that occur in the transmission ... [more ▼]

In this paper, automatic fault diagnosis in large scale power networks described by second-order nonlinear swing equations is studied. This work focuses on a class of faults that occur in the transmission lines. Transmission line protection is an important issue in power system engineering because a large portion of power system faults is occurring in transmission lines. This paper presents a novel technique to detect, isolate and identify the faults on transmissions using only a small number of observations. We formulate the problem of fault diagnosis of nonlinear power network into a compressive sensing framework and derive an optimisationbased formulation of the fault identification problem. An iterative reweighted `1-minimisation algorithm is finally derived to solve the detection problem efficiently. Under the proposed framework, a real-time fault monitoring scheme can be built using only measurements of phase angles of nonlinear power networks. [less ▲]

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See detailDynamical Structure Function and Granger Causality: Similarities and Differences
Yue, Zuogong UL; Thunberg, Johan UL; Yuan, Ye et al

in 54th IEEE Conference on Decision and Control, Osaka, Japan, December 15-18, 2015 (2015)

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See detailModelling the natural history of Huntingtons disease progression
Kuan, William; Kasis, Andrea; Yuan, Ye et al

in BMJ Open (2014)

Background: The lack of reliable biomarkers to track disease progression is a major problem in clinical research of chronic neurological disorders. Using Huntington’s disease (HD) as an example, we ... [more ▼]

Background: The lack of reliable biomarkers to track disease progression is a major problem in clinical research of chronic neurological disorders. Using Huntington’s disease (HD) as an example, we describe a novel approach to model HD and show that the progression of a neurological disorder can be predicted for individual patients. Methods : Starting with an initial cohort of 343 patients with HD that we have followed since 1995, we used data from 68 patients that satisfied our filtering criteria to model disease progression, based on the Unified Huntington’s Disease Rating Scale (UHDRS), a measure that is routinely used in HD clinics worldwide. Results : Our model was validated by: (A) extrapolating our equation to model the age of disease onset, (B) testing it on a second patient data set by loosening our filtering criteria, (C) cross-validating with a repeated random subsampling approach and (D) holdout validating with the latest clinical assessment data from the same cohort of patients. With UHDRS scores from the past four clinical visits (over a minimum span of 2 years), our model predicts disease progression of individual patients over the next 2 years with an accuracy of 89–91%. We have also provided evidence that patients with similar baseline clinical profiles can exhibit very different trajectories of disease progression. Conclusions : This new model therefore has important implications for HD research, most obviously in the development of potential disease-modifying therapies. We believe that a similar approach can also be adapted to model disease progression in other chronic neurological disorders. [less ▲]

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See detailH2-Based Network Volatility Measures
Goncalves, Jorge UL; Huang, Qingqing; Yuan, Ye et al

in American Control Conference (2014, June)

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See detailNetwork Reconstruction from Intrinsic Noise: Non-Minimum-Phase Systems
Hayden, David; Yuan, Ye; Goncalves, Jorge UL

in The proceedings of the The 19th World Congress of The International Federation of Automatic Control (2014)

This paper considers the problem of inferring the structure and dynamics of an unknown network driven by unknown noise inputs. Equivalently we seek to identify direct causal dependencies among manifest ... [more ▼]

This paper considers the problem of inferring the structure and dynamics of an unknown network driven by unknown noise inputs. Equivalently we seek to identify direct causal dependencies among manifest variables only from observations of these variables. We consider linear, time-invariant systems of minimal order and with one noise source per manifest state. It is known that if the transfer matrix from the inputs to manifest states is minimum phase, then this problem has a unique solution, irrespective of the network topology. Here we consider the general case where the transfer matrix may be non-minimum phase and show that solutions are characterized by an Algebraic Riccati Equation (ARE). Each solution to the ARE corresponds to at most one spectral factor of the output spectral density that satisfies the assumptions made. Hence in general the problem may not have a unique solution, but all solutions can be computed by solving an ARE and their number may be finite. [less ▲]

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See detailFinite-time road grade computation for a vehicle platoon
Yang, Tao; Yuan, Ye; Li, Kezhi et al

in IEEE (2014)

Given a platoon of vehicles traveling uphill, this paper considers the finite-time road grade computation problem. We propose a decentralized algorithm for an arbitrarily chosen vehicle to compute the ... [more ▼]

Given a platoon of vehicles traveling uphill, this paper considers the finite-time road grade computation problem. We propose a decentralized algorithm for an arbitrarily chosen vehicle to compute the road grade in a finite number of time-steps by using only its own successive velocity measurements. Simulations then illustrate the theoretical results. These new results can be applied to real-world vehicle platooning problems to reduce fuel consumption and carbon dioxide emissions. [less ▲]

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See detailNetwork Reconstruction from Intrinsic Noise
Hayden, David; Yuan, Ye; Goncalves, Jorge UL

in The proceedings of the American Control Conference (2014)

This paper considers the problem of inferring the structure and dynamics of an unknown network driven by unknown noise inputs. Equivalently we seek to identify direct causal dependencies among manifest ... [more ▼]

This paper considers the problem of inferring the structure and dynamics of an unknown network driven by unknown noise inputs. Equivalently we seek to identify direct causal dependencies among manifest variables only from observations of these variables. We consider linear, time-invariant systems of minimal order and with one noise source per measured state. If the transfer matrix from the inputs to manifest states is known to be minimum phase, this problem is shown to have a unique solution irrespective of the network topology. This is equivalent to there being only one spectral factor (up to a choice of signs of the inputs) of the output spectral density that satisfies these assumptions. Hence for this significant class of systems, the network reconstruction problem is well posed. [less ▲]

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See detailH2 Norm Based Network Volatility Measures
Huang, Qingqing; Yuan, Ye; Goncalves, Jorge UL et al

in The proceedings of the American Control Conference (2014)

Motivated by applications in biology and economics, we propose new volatility measures based on the H2 system norm for linear networks stimulated by independent or correlated noise. We identify critical ... [more ▼]

Motivated by applications in biology and economics, we propose new volatility measures based on the H2 system norm for linear networks stimulated by independent or correlated noise. We identify critical links in a network, where relatively small improvements can lead to large reductions in network volatility measures. We also examine volatility measures of individual nodes and their dependence on the topological position in the network. Finally, we investigate the dependence of the volatility on different network interconnections, weights of the edges and other network properties. Hence in an intuitive and efficient way, we can identify critical links, nodes and interconnections in network which can shed light in the network design to make it more robust. [less ▲]

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See detailDecentralised minimum-time consensus
Yuan, Ye; Stan, Guy-Bart; Shi, Ling et al

in Automatica (2013), 49(5), 1227-1235

We consider the discrete-time dynamics of a network of agents that exchange information according to a nearest-neighbour protocol under which all agents are guaranteed to reach consensus asymptotically ... [more ▼]

We consider the discrete-time dynamics of a network of agents that exchange information according to a nearest-neighbour protocol under which all agents are guaranteed to reach consensus asymptotically. We present a fully decentralised algorithm that allows any agent to compute the final consensus value of the whole network in finite time using the minimum number of successive values of its own state history. We show that the minimum number of steps is related to a Jordan block decomposition of the network dynamics, and present an algorithm to compute the final consensus value in the minimum number of steps by checking a rank condition of a Hankel matrix of local observations. Furthermore, we prove that the minimum number of steps is related to graph theoretical notions that can be directly computed from the Laplacian matrix of the graph and from the minimum external equitable partition. [less ▲]

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See detailReal-time Fault Diagnosis for Large-Scale Nonlinear Power Networks
Pan, Wei UL; Yuan, Ye; Sandberg, Henrik et al

in The proceedings of the IEEE 52nd Annual Conference on Decision and Control (2013)

In this paper, automatic fault diagnosis in large scale power networks described by second-order nonlinear swing equations is studied. This work focuses on a class of faults that occur in the transmission ... [more ▼]

In this paper, automatic fault diagnosis in large scale power networks described by second-order nonlinear swing equations is studied. This work focuses on a class of faults that occur in the transmission lines. Transmission line protection is an important issue in power system engineering because a large portion of power system faults is occurring in transmission lines. This paper presents a novel technique to detect, isolate and identify the faults on transmissions using only a small number of observations. We formulate the problem of fault diagnosis of nonlinear power network into a compressive sensing framework and derive an optimisation-based formulation of the fault identification problem. An iterative reweighted ℓ1-minimisation algorithm is finally derived to solve the detection problem efficiently. Under the proposed framework, a real-time fault monitoring scheme can be built using only measurements of phase angles of nonlinear power networks. [less ▲]

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See detailNetwork reconstruction using knock-out and over-expression data
Hayden, David; Yuan, Ye; Goncalves, Jorge UL

in The proceedings of the 2013 European Control Conference (ECC) (2013)

This paper outlines necessary and sufficient conditions for network reconstruction of linear, time-invariant systems using data from either knock-out or over-expression experiments. These structural ... [more ▼]

This paper outlines necessary and sufficient conditions for network reconstruction of linear, time-invariant systems using data from either knock-out or over-expression experiments. These structural system perturbations, which are common in biological experiments, can be formulated as unknown system inputs, allowing the network topology and dynamics to be found. We assume that only partial state measurements are available and propose an algorithm that can reconstruct the network at the level of the measured states using either time-series or steady-state data. A simulated example illustrates how the algorithm successfully reconstructs a network from data. [less ▲]

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See detailDistributed Kalman Filter with minimum-time covariance computation
Thia, Jerry; Yuan, Ye; Shi, Ling et al

in The proceedings of the IEEE 52nd Annual Conference on Decision and Control (2013)

This paper considerably improves the well-known Distributed Kalman Filter (DKF) algorithm by Olfati-Saber (2007) by introducing a novel decentralised consensus value computation scheme, using only local ... [more ▼]

This paper considerably improves the well-known Distributed Kalman Filter (DKF) algorithm by Olfati-Saber (2007) by introducing a novel decentralised consensus value computation scheme, using only local observations of sensors. It has been shown that the state estimates obtained in [8] and [9] approaches those of the Central Kalman Filter (CKF) asymptotically. However, the convergence to the CKF can sometimes be too slow. This paper proposes an algorithm that enables every node in a sensor network to compute the global average consensus matrix of measurement noise covariance in minimum time without accessing global information. Compared with the algorithm in [8], our theoretical analysis and simulation results show that the new algorithm can offer improved performance in terms of time taken for the state estimates to converge to that of the CKF. [less ▲]

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See detailEARLY FLOWERING4 Recruitment of EARLY FLOWERING3 in the Nucleus Sustains the Arabidopsis Circadian Clock
Herrero, Eva; Kolmos, Elsebeth; Bujdoso, Nora et al

in Plant Cell (2012), 24(2), 428-443

The plant circadian clock is proposed to be a network of several interconnected feedback loops, and loss of any component leads to changes in oscillator speed. We previously reported that Arabidopsis ... [more ▼]

The plant circadian clock is proposed to be a network of several interconnected feedback loops, and loss of any component leads to changes in oscillator speed. We previously reported that Arabidopsis thaliana EARLY FLOWERING4 (ELF4) is required to sustain this oscillator and that the elf4 mutant is arrhythmic. This phenotype is shared with both elf3 and lux. Here, we show that overexpression of either ELF3 or LUX ARRHYTHMO (LUX) complements the elf4 mutant phenotype. Furthermore, ELF4 causes ELF3 to form foci in the nucleus. We used expression data to direct a mathematical position of ELF3 in the clock network. This revealed direct effects on the morning clock gene PRR9, and we determined association of ELF3 to a conserved region of the PRR9 promoter. A cis-element in this region was suggestive of ELF3 recruitment by the transcription factor LUX, consistent with both ELF3 and LUX acting genetically downstream of ELF4. Taken together, using integrated approaches, we identified ELF4/ELF3 together with LUX to be pivotal for sustenance of plant circadian rhythms. [less ▲]

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