Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Just-in-time learning (JITL) is a commonly used technique for industrial soft sensing of nonlinear processes. Hover, traditional JITL approaches mainly focus on equal sample sizes beten process (input) variables and quality (output) variables, which may not be practical in industrial processes since quality variables are usually much harder to obtain than other process variables. In order to handle unequal length dataset with only a few labeled data, a novel semisupervised JITL framework is proposed for soft sensor modeling for nonlinear processes, which is based on semisupervised ighted probabilistic principal component regression (SWPPCR). In the new semisupervised JITL framework, traditional Mahalanobis distance and a new proposed scaled Mahalanobis distance are used for similarity measurement and ight assignment. By selecting the most relevant labeled and unlabeled samples and assigning them with the corresponding ights, a local SWPPCR can be built to estimate the output variables of the query sample. Case studies are carried out to evaluate the prediction performance of the proposed semisupervised JITL framework on a numerical example and an industrial process. The effectiveness and flexibility of the proposed method are demonstrated by the prediction results.
['Xiaofeng Yuan', 'Zhiqiang Ge', 'Biao Huang', 'Zhihuan Song', 'Yalin Wang']
I am writing a research paper on a idea Semisupervised JITL Framework for Nonlinear Industrial Soft Sensing Based on Locally Semisupervised Weighted PCR
[{"from": "human", "value": "I am writing a research paper on a idea Semisupervised JITL Framework for Nonlinear Industrial Soft Sensing Based on Locally Semisupervised Weighted PCR"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Just-in-time learning (JITL) is a commonly used technique for industrial soft sensing of nonlinear processes. Hover, traditional JITL approaches mainly focus on equal sample sizes beten process (input) variables and quality (output) variables, which may not be practical in industrial processes since quality variables are usually much harder to obtain than other process variables. In order to handle unequal length dataset with only a few labeled data, a novel semisupervised JITL framework is proposed for soft sensor modeling for nonlinear processes, which is based on semisupervised ighted probabilistic principal component regression (SWPPCR). In the new semisupervised JITL framework, traditional Mahalanobis distance and a new proposed scaled Mahalanobis distance are used for similarity measurement and ight assignment. By selecting the most relevant labeled and unlabeled samples and assigning them with the corresponding ights, a local SWPPCR can be built to estimate the output variables of the query sample. Case studies are carried out to evaluate the prediction performance of the proposed semisupervised JITL framework on a numerical example and an industrial process. The effectiveness and flexibility of the proposed method are demonstrated by the prediction results."}]