Diamond cutting-tool wear has a direct impact on the processing accuracy of machined surface in ultra-precision diamond cutting. It is difficult to monitor tool’s condition because slight amount. This paper proposed hybrid deep learning model for tool state prediction The cutting force was accurately estimated and predicted by using with motion displacement, velocity, other signals machining pr...

The improvement of ultra-precision machining technology has significantly boosted the demand for surface quality and accuracy workpieces to be machined. However, geometric shapes workpiece surfaces cannot adequately manufactured with simple plane, cylindrical, or spherical because their different applications in various fields. In this research, a method was proposed generate tool paths complex...

The LOGON MT demonstrator assembles independently valuable general-purpose NLP components into a machine translation pipeline that capitalizes on output quality. The demonstrator embodies an interesting combination of hand-built, symbolic resources and stochastic processes.

Prompted by feedback from several partners in the industry, Sun is proposing a change to the specification of floating-point in the Java programming language. The current Java programming language and virtual machine specifications require that all single and double precision floating-point calculations must round their results to the IEEE 754 single and double precision formats, respectively. ...

In this paper we present an approach to coreference resolution that integrates empirical methods with machine learning techniques. This approach departs from previous solutions for reference resolution, in that it promotes data-driven techniques instead of relying on combinations of linguistic and cognitive aspects of discourse. The immediate pragmatic result is an enhancement of precision and ...

Interpretability of machine learning models is critical for data-driven precision medicine efforts. However, highly predictive models are generally complex and are difficult to interpret. Here using Model-Agnostic Explanations algorithm, we show that complex models such as random forest can be made interpretable. Using MIMIC-II dataset, we successfully predicted ICU mortality with 80% balanced ...