Novel virtual sample generation using conditional GAN for developing soft sensor with small data

[1]  Yan-Lin He,et al.  A Monte Carlo and PSO based virtual sample generation method for enhancing the energy prediction and energy optimization on small data problem: An empirical study of petrochemical industries , 2017 .

[2]  Yoshua Bengio,et al.  Generative Adversarial Nets , 2014, NIPS.

[3]  Luigi Fortuna,et al.  Comparison of Soft-Sensor Design Methods for Industrial Plants Using Small Data Sets , 2009, IEEE Transactions on Instrumentation and Measurement.

[4]  Zhiqiang Ge,et al.  Soft Sensor Modeling of Nonlinear Industrial Processes Based on Weighted Probabilistic Projection Regression , 2017, IEEE Transactions on Instrumentation and Measurement.

[5]  Der-Chiang Li,et al.  A tree-based-trend-diffusion prediction procedure for small sample sets in the early stages of manufacturing systems , 2012, Expert Syst. Appl..

[6]  Yan-Lin He,et al.  Soft-sensing model development using PLSR-based dynamic extreme learning machine with an enhanced hidden layer , 2016 .

[7]  Saeid Minaei,et al.  Vision-based pest detection based on SVM classification method , 2017, Comput. Electron. Agric..

[8]  Qunxiong Zhu,et al.  Dealing with small sample size problems in process industry using virtual sample generation: a Kriging-based approach , 2020, Soft Comput..

[9]  Hung-Chang Hsu,et al.  A new approach to prediction of radiotherapy of bladder cancer cells in small dataset analysis , 2011, Expert Syst. Appl..

[10]  Jeff Donahue,et al.  Large Scale GAN Training for High Fidelity Natural Image Synthesis , 2018, ICLR.

[11]  Marco Zaffalon,et al.  Bayesian network data imputation with application to survival tree analysis , 2016, Comput. Stat. Data Anal..

[12]  Yan-Lin He,et al.  A novel and effective nonlinear interpolation virtual sample generation method for enhancing energy prediction and analysis on small data problem: A case study of Ethylene industry , 2018 .

[13]  Weihua Gui,et al.  A Layer-Wise Data Augmentation Strategy for Deep Learning Networks and Its Soft Sensor Application in an Industrial Hydrocracking Process , 2019, IEEE Transactions on Neural Networks and Learning Systems.

[14]  Der-Chiang Li,et al.  A forecasting model for small non-equigap data sets considering data weights and occurrence possibilities , 2014, Comput. Ind. Eng..

[15]  Zhiqiang Ge,et al.  Online Updating Soft Sensor Modeling and Industrial Application Based on Selectively Integrated Moving Window Approach , 2017, IEEE Transactions on Instrumentation and Measurement.

[16]  Saeid Shokri,et al.  Combination of data rectification techniques and soft sensor model for robust prediction of sulfur content in HDS process , 2016 .

[17]  Hans-Peter Kriegel,et al.  LOF: identifying density-based local outliers , 2000, SIGMOD '00.

[18]  Sifeng Liu,et al.  Grey system model with the fractional order accumulation , 2013, Commun. Nonlinear Sci. Numer. Simul..

[19]  Xiao Wang,et al.  Data Preprocessing for Soft Sensor Using Generative Adversarial Networks , 2018, 2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV).

[20]  Yuan Xu,et al.  A virtual sample generation approach based on a modified conditional GAN and centroidal Voronoi tessellation sampling to cope with small sample size problems: Application to soft sensing for chemical process , 2021, Appl. Soft Comput..

[21]  Rui Yao,et al.  A novel intelligent diagnosis method using optimal LS-SVM with improved PSO algorithm , 2017, Soft Computing.

[22]  Xiao Wang,et al.  Data supplement for a soft sensor using a new generative model based on a variational autoencoder and Wasserstein GAN , 2020 .

[23]  Christian Ledig,et al.  Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[24]  Shou-De Lin,et al.  MetricGAN: Generative Adversarial Networks based Black-box Metric Scores Optimization for Speech Enhancement , 2019, ICML.

[25]  T. Poggio,et al.  Recognition and Structure from one 2D Model View: Observations on Prototypes, Object Classes and Symmetries , 1992 .

[26]  Der-Chiang Li,et al.  Using mega-trend-diffusion and artificial samples in small data set learning for early flexible manufacturing system scheduling knowledge , 2007, Comput. Oper. Res..

[27]  Yanlin He,et al.  Enhanced virtual sample generation based on manifold features: Applications to developing soft sensor using small data. , 2021, ISA transactions.

[28]  Wenqing Wu,et al.  The conformable fractional grey system model. , 2018, ISA transactions.