Operations Research, Systems Engineering and Industrial Engineering Commons^™

Data Generation Model-Based Synthetic Sample Imputation Method, Yulin He, Jiaqi Chen, Hepeng Xu, Zhexue Huang, Jianfei Yin

Journal of System Simulation

Abstract: In order to solve the problem of inconsistent probability distribution between synthetic samples by imputation and real samples, a data generation model-based synthetic sample imputation (DGM-SSI) method is proposed. The data generation model of real samples is constructed based on the Gaussian mixture model, and the number of corresponding components of the Gaussian mixture model is determined by the multi-model fusion strategy. The synthetic samples required for model imputation are generated by using the data obtained from the real samples. Specifically, the components of the data generation model and their weights are used to control the generation of synthetic …

Outlier Detection During Thermal Processes Based On Improved Gaussian Mixture Model, Zheng Wu, Yue Zhang, Ze Dong

Journal of System Simulation

Abstract: Abnormal data detection during thermal processes is the basis for performing system modeling, control, and optimization and constitutes an important part of data processing. In this paper, an unsupervised outlier detection algorithm during thermal processes based on an improved Gaussian mixture model is proposed. The algorithm captures a class of data clusters under specific working conditions by using Gaussian components in each dimension, modifies the posterior probability density of the traditional model by adding penalty constraint factors to penalize the false detection and missed detection items, and identifies abnormal data according to the correlation differences with the …

A Just-In-Time Learning Soft Sensing Modeling Method Based On Bayesian Gaussian Mixture Model, Qi Cheng, Weili Xiong

Journal of System Simulation

Abstract: For some time-varying industrial processes with non-Gaussian properties, the model established by the general soft-sensing method is difficult to meet the accuracy requirement. To solve the above problems effectively, a JITL soft sensor modeling method (BGMM) is proposed based on Bayesian Gaussian Mixture Model. For the given training sample set, the number of components of the Gaussian mixture model is optimized by Bayesian Information Criterion (BIC); For new test samples, Gaussian Process Regression (GPR) model is established by using the BGMM similarity criterion for the training samples to find out the most similar set;The model is used to predict …

A Hierarchical Integrated Soft Sensing Modeling Method For Gauss Process Regression, Zhao Shuai, Xudong Shi, Weili Xiong

Journal of System Simulation

Abstract: Chemical processes are often characterized by nonlinearity and multi-phase, a soft sensor model based on the hierarchical ensemble of Gaussian process regression is proposed. First, the Gaussian mixture model is used to divide the process data into different operation phases. Then, the principal component analysis of each stage is carried out, and the model data are divided into several subspaces, according to the contribution of each auxiliary variable in the principal component space, and the corresponding Gaussian process regression model is built. The subspace model output is fused by means to obtain the first level ensemble output. Finally, the …

Face Skin Detection And Color Transferring, Fangfang Chen, Zhongping Ji

Journal of System Simulation

Abstract: The reliable and accurate extraction of facial skin is a key and urgent problem for skin detection. This paper proposes a face alignment and GMM (Gaussian mixture model) to achieve face skin detection. We use the SCUT-FBP5500 face database, combined with SOM (self-organizing Maps) and K-means clustering method to cluster 9 pairs of facial features, and then use ResNet50 to predict face score. The color migration algorithm based on Lab space is used to migrate and edit the face color.The facts prove that the method has certain effect.

Embedded Fault Class Detection Methodology For Condition-Based Machine Monitoring And Predictive Maintenance, Nagdev Amruthnath

Dissertations

Ever since the Second Industrial Revolution, manufacturing firms have continuously been working on minimizing the inefficiencies and maximizing the productivity of their system. This objective led to the creation of the Toyota Production System which follows the motto of “making [the] highest quality products at the least cost in the shortest lead time. (Ohno, 1988)” This philosophy is widely recognized and is utilized by various industries today.

Currently, we are going through the Fourth Industrial Revolution (also called, Industry 4.0) where internet technologies are utilized to additionally maximize the productivity in the production processes. Process synchronization is one of the …