Haitao Liu
Nanyang Technological University
HeteroscedasticityBayesian probabilityAlgorithmMachine learningData miningMathematical optimizationEngineeringArtificial intelligenceBayesian inferenceInferenceApproximate inferenceCommittee machineTime complexityMathematicsEngineering design processComputer scienceScalabilityArtificial neural networkLatent variableMechanicsGlobal Positioning SystemGaussian processBig dataKrigingMetamodelingSampling (statistics)Centrifugal compressor
37Publications
14H-index
696Citations
Publications 37
Newest
Last. Xiaofang WangH-Index: 20
view all 6 authors...
The radial-flow turbine, a key component of the supercritical CO2 ( S-CO2) Brayton cycle, has a significant impact on the cycle efficiency. The inlet volute is an important flow component that introduces working fluid into the centripetal turbine. In-depth research on it will help improve the performance of the turbine and the entire cycle. This article aims to improve the volute flow capacity by optimizing the cross-sectional geometry of the volute, thereby improving the volute performance, bot...
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#1Haitao Liu (DUT: Dalian University of Technology)H-Index: 14
#2Jiaqi Ding (DUT: Dalian University of Technology)
Last. Xiaofang Wang (DUT: Dalian University of Technology)H-Index: 20
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Multi-task regression attempts to exploit the task similarity in order to achieve knowledge transfer across related tasks for performance improvement. The application of Gaussian process (GP) in this scenario yields the non-parametric yet informative Bayesian multi-task regression paradigm. Multi-task GP (MTGP) provides not only the prediction mean but also the associated prediction variance to quantify uncertainty, thus gaining popularity in various scenarios. The linear model of coregionalizat...
#1Zhendong Guo (Xi'an Jiaotong University)H-Index: 1
#2Yew-Soon Ong (NTU: Nanyang Technological University)H-Index: 64
Last. Haitao Liu (DUT: Dalian University of Technology)H-Index: 14
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Expected improvement (EI), a function of prediction uncertainty null null null null null null null null null null null null null null null null null null null null null null null null null null null null null null null null null null null null null null null null null null null null null null null null null null null null null null null null null null $\sigma (\mathbf{x}) null null null null null null null null null null null null null null null null null null null null null null null null nu...
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#1Haitao Liu (DUT: Dalian University of Technology)H-Index: 14
#2Changjun Liu (DUT: Dalian University of Technology)
Last. Xiaofang Wang (DUT: Dalian University of Technology)H-Index: 20
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The demand of probabilistic time series forecasting has been recently raised in various dynamic system scenarios, for example, system identification and prognostic and health management of machines. To this end, we combine the advances in both deep generative models and state space model (SSM) to come up with a novel, data-driven deep probabilistic sequence model. Specially, we follow the popular encoder-decoder generative structure to build the recurrent neural networks (RNN) assisted variation...
Deep kernel learning (DKL) leverages the connection between Gaussian process (GP) and neural networks (NN) to build an end-to-end, hybrid model. It combines the capability of NN to learn rich representations under massive data and the non-parametric property of GP to achieve automatic regularization that incorporates a trade-off between model fit and model complexity. However, the deterministic encoder may weaken the model regularization of the following GP part, especially on small datasets, du...
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#2Haitao Liu (DUT: Dalian University of Technology)H-Index: 14
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#1Haitao Liu (NTU: Nanyang Technological University)H-Index: 14
#2Yew-Soon Ong (NTU: Nanyang Technological University)H-Index: 64
Last. Jianfei CaiH-Index: 50
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Heteroscedastic regression considering the varying noises among observations has many applications in the fields, such as machine learning and statistics. Here, we focus on the heteroscedastic Gaussian process (HGP) regression that integrates the latent function and the noise function in a unified nonparametric Bayesian framework. Though showing remarkable performance, HGP suffers from the cubic time complexity, which strictly limits its application to big data. To improve the scalability, we fi...
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#2Haitao Liu (DUT: Dalian University of Technology)H-Index: 14
#3Zhitao TianH-Index: 1
Last. Xiaofang WangH-Index: 20
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#5Xiaobo Shen (Nanjing University of Science and Technology)H-Index: 12
Gaussian process classification (GPC) provides a flexible and powerful statistical framework describing joint distributions over function space. Conventional GPCs, however, suffer from: 1) poor scalability for big data due to the full kernel matrix and 2) intractable inference due to the non-Gaussian likelihoods. Hence, various scalable GPCs have been proposed through: 1) the sparse approximation built upon a small inducing set to reduce the time complexity and 2) the approximate inference to de...
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#1Lusheng ZhouH-Index: 1
#2Xiaofang WangH-Index: 20
Last. Jianchi XinH-Index: 2
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The annular seal between the stator and rotor substantively act as a bearing which affects the rotordynamic characteristic of the Reactor Coolant Pump (RCP) during its long-time service. The target of this work is to have a major research about the operational conditions effects about inlet pressure and rotational speeds on the rotordynamic characteristics of the annular seal in the RCP. For solving the annular seal frequency-dependent rotordynamic coefficients, a 3D numerical method applying me...
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