題目：Engineering-Knowledge-Driven Statistical Modeling for Spatial Data
時(shí)間：2015.4.14（星期二）上午10:00
主講人：王凱波 副教授（清華大學(xué)工業(yè)工程系）
地點(diǎn)：主樓216
主講人介紹：
王凱波博士是清華大學(xué)工業(yè)工程系的副教授。他在香港科技大學(xué)獲得工業(yè)工程與工程管理學(xué)博士學(xué)位。王凱波的研究主要關(guān)注復(fù)雜系統(tǒng)的質(zhì)量建模、監(jiān)視與控制。他是多個(gè)自然科學(xué)基金與企業(yè)資助科研項(xiàng)目的負(fù)責(zé)人，在質(zhì)量控制領(lǐng)域SCI索引的國(guó)際期刊發(fā)表了30余篇論文，其中包括Journal of Quality Technology, IIE Transactions, IEEE Transactions of Automation Science and Engineering, Quality and Reliability Engineering International等。
內(nèi)容介紹：
In certain complex manufacturing systems, the quality of a product is adequately characterized by a high-dimensional data map rather than by single or multiple variables. Such data maps also preserve unique spatial structures. Therefore, variation pattern analysis and statistical modeling based on the data map become very important for enhanced process understanding and quality improvement.
Using a real wafer example from semiconductor manufacturing and a carbon nano tube example from nano-manufacturing, we demonstrate how statistical models can be developed by incorporating engineering knowledge. In the wafer example, a three-stage hierarchical model is proposed. The wafer surface variation is decomposed into the macro- and micro-scale variations, which are modeled as a cubic curve and a first-order intrinsic Gaussian Markov random field, respectively. In the carbon nano tube example, a piece-wise polynomial model with spatial auto-regressive disturbance is developed. These examples show that engineering knowledge driven statistical modeling can play an important role in quality control of complex systems, and is also a promising area for statistical research.
（主辦：管理工程系）
