Machining process classification using PCA reduced histogram features and the support vector machine

Being able to identify machining processes that produce specific machined surfaces is crucial in modern manufacturing production. Image processing and computer vision technologies have become indispensable tools for automated identification with benefits such as reduction in inspection time and avoidance of human errors due to inconsistency and fatigue. In this paper, the Support Vector Machine (SVM) classifier with various kernels is investigated for the categorization of machined surfaces into the six machining processes of Turning, Grinding, Horizontal Milling, Vertical Milling, Lapping, and Shaping. The effectiveness of the gray-level histogram as the discriminating feature is explored. Experimental results suggest that the SVM with the linear kernel provides superior performance for a dataset consisting of 72 workpiece images

On the Concept of Quantum State Reduction: Inconsistency of the Orthodox View

The argument is re-examined that the program of deriving the rule of state reduction from the Schroedinger equation holding for the object-apparatus composite system falls into a vicious circle or an infinite regress called the von Neumann chain. It is shown that this argument suffers from a serious physical inconsistency concerning the causality between the reading of the outcome and the state reduction. A consistent argument which accomplishes the above program without falling into the circular argument is presented.Comment: 15 pages, LaTeX, 1 Postscript figur