Pinned Repositories
-
Mutual Information Neural Estimator implemented in Tensorflow
-tensor-
Repository for paper 'Truncated tensor Schatten p-norm based approach for spatiotemporal traffic data imputation with complicated missing patterns'.
-wwwww
List of papers, code and experiments using deep learning for time series forecasting
axidma
AXI DMA Check: A utility to measure DMA speeds in simulation
DMTO-Catalyst-Carbon-Deposition-EDBR-JITL
Dimethyl ether/Methanol to Olefins (DMTO) is one of the important unit in coal chemical industry, and the distribution of its reaction products can be regulated and optimized by catalyst carbon deposition. Aiming at the disadvantages of time-consuming and high-cost analysis of traditional catalyst carbon deposition measurement methods, a Just-in-Time Learning (JITL) soft sensor model based on Euclidean Distance similarity and Bayesian Regression local model (EDBR-JITL) is proposed for DMTO catalyst carbon deposition in this paper. In the proposed model, the mapping relationship between process state, operating parameters, and catalyst carbon deposition is established, and catalyst carbon deposition is predicted at sampling time by a linear local model (BR) which is obtained by the ED similarity. The results of an industrial DMTO example show that the mean absolute error (MAE) between the predicted value of EDBR-JITL online and the real value of measurement offline for catalyst carbon deposition is 0.10, the mean absolute percentage error (MAPE) is 1.46%, the coefficient of determination (R 2 ) is 0.92, and the prediction time is only in milliseconds. In summary, catalyst carbon deposition in DMTO process is not only predicted and monitored online accurately and conveniently by the proposed EDBR-JITL method, but also is regulated and optimized as the linear BR local model can return the weight of each input parameter at each sampling time.
Inferential_Sensor_Experiment
Soft Sensor with Variational Inference Technique
IT_book
本项目收藏这些年来看过或者听过的一些不错的常用的上千本书籍，没准你想找的书就在这里呢，包含了互联网行业大多数书籍和面试经验题目等等。有人工智能系列（常用深度学习框架TensorFlow、pytorch、keras。NLP、机器学习，深度学习等等），大数据系列(Spark,Hadoop,Scala,kafka等)，程序员必修系列（C、C++、java、数据结构、linux，设计模式、数据库等等）
loveBalloon
:balloon:塞纳河畔，左岸的咖啡。告白气球，飞入我的心扉。https://ajlovechina.github.io/loveBalloon/.
LSTM-SVM-RF-time-series
Regression prediction of time series data using LSTM, SVM and random forest. 使用LSTM、SVM、随机森林对时间序列数据进行回归预测，注释拉满。
Machine-Learning
Notes for machine learning
AIXIE's Repositories
AIXIE/LSTM-SVM-RF-time-series
Regression prediction of time series data using LSTM, SVM and random forest. 使用LSTM、SVM、随机森林对时间序列数据进行回归预测，注释拉满。
AIXIE/-
Mutual Information Neural Estimator implemented in Tensorflow
AIXIE/-tensor-
Repository for paper 'Truncated tensor Schatten p-norm based approach for spatiotemporal traffic data imputation with complicated missing patterns'.
AIXIE/-wwwww
List of papers, code and experiments using deep learning for time series forecasting
AIXIE/axidma
AXI DMA Check: A utility to measure DMA speeds in simulation
AIXIE/DMTO-Catalyst-Carbon-Deposition-EDBR-JITL
Dimethyl ether/Methanol to Olefins (DMTO) is one of the important unit in coal chemical industry, and the distribution of its reaction products can be regulated and optimized by catalyst carbon deposition. Aiming at the disadvantages of time-consuming and high-cost analysis of traditional catalyst carbon deposition measurement methods, a Just-in-Time Learning (JITL) soft sensor model based on Euclidean Distance similarity and Bayesian Regression local model (EDBR-JITL) is proposed for DMTO catalyst carbon deposition in this paper. In the proposed model, the mapping relationship between process state, operating parameters, and catalyst carbon deposition is established, and catalyst carbon deposition is predicted at sampling time by a linear local model (BR) which is obtained by the ED similarity. The results of an industrial DMTO example show that the mean absolute error (MAE) between the predicted value of EDBR-JITL online and the real value of measurement offline for catalyst carbon deposition is 0.10, the mean absolute percentage error (MAPE) is 1.46%, the coefficient of determination (R 2 ) is 0.92, and the prediction time is only in milliseconds. In summary, catalyst carbon deposition in DMTO process is not only predicted and monitored online accurately and conveniently by the proposed EDBR-JITL method, but also is regulated and optimized as the linear BR local model can return the weight of each input parameter at each sampling time.
AIXIE/Inferential_Sensor_Experiment
Soft Sensor with Variational Inference Technique
AIXIE/IT_book
本项目收藏这些年来看过或者听过的一些不错的常用的上千本书籍，没准你想找的书就在这里呢，包含了互联网行业大多数书籍和面试经验题目等等。有人工智能系列（常用深度学习框架TensorFlow、pytorch、keras。NLP、机器学习，深度学习等等），大数据系列(Spark,Hadoop,Scala,kafka等)，程序员必修系列（C、C++、java、数据结构、linux，设计模式、数据库等等）
AIXIE/loveBalloon
:balloon:塞纳河畔，左岸的咖啡。告白气球，飞入我的心扉。https://ajlovechina.github.io/loveBalloon/.
AIXIE/Machine-Learning
Notes for machine learning
AIXIE/notes
一个码农的毕生所学.考研,就业,上学.语言篇，Android,C++,Java,JavaScript,Latex,MATLAB,NodeJS,PHP,Python,技术篇,docker,git,Linux,Maven,office,Spark,Spring,SVN,基础篇,编译原理,操作系统,单片机,计算机网络,计算机网络实验,架构模式,软件文档写作,设计模式,数据结构,数据库,算法,UML建模,Windows程序设计,数学篇,概率论与数理统计,微积分,线性代数,张量,机器学习篇,机器学习,pytorch,sklearn,TensorFlow
AIXIE/paper-reading
深度学习经典、新论文逐段精读
AIXIE/pytorch-vae
A Variational Autoencoder (VAE) implemented in PyTorch
AIXIE/PyTorch-VAE-1
A Collection of Variational Autoencoders (VAE) in PyTorch.
AIXIE/Regression
Metro Interstate Traffic Volume
AIXIE/SAE_pytorch
基于pytorch实现的堆叠自编码神经网络，包含网络模型构造、训练、测试
AIXIE/Soft-Sensor-Modelling
Soft sensor modelling using multiple machine learning algorithms
AIXIE/SVR_MLR_DTR_BP
SVR, Decision Tree, Rendom Forest, Multi Linear Regression, Polynomial Regression
AIXIE/TCN
Sequence modeling benchmarks and temporal convolutional networks
AIXIE/tinyriscv
A very simple and easy to understand RISC-V core.
AIXIE/transferlearning
Everything about Transfer Learning and Domain Adaptation--迁移学习
AIXIE/verilog-axi
Verilog AXI components for FPGA implementation
AIXIE/waixie
老王的仓库