Inside IISE Journals

This month we highlight two articles from IISE Transactions.The first article develops a sign-constrained statistical learning method by innovatively integrating physical knowledge and data science modeling methods for quantifying the wake effect characteristics. The proposed method is used to predict the wake effect of wind turbines with real data sets and demonstrated significant improvement in reducing prediction error compared with only using physical model or purely data-driven methods. The second article develops a connected-path filtering method to detect systematic patterns of wafers, which helps find the root causes of failure to find the right interventions for quality management. The method was tested on real wafer bin map data from SK hynix with great success. These articles will appear in the February 2018 issue of IISE Transactions (Vol 50, No. 2).

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