Xiaoli Li

Learn More
—Tool wear condition monitoring has the potential to play a critical role in ensuring the dimensional accuracy of the workpiece and prevention of damage to cutting equipment. It could also help in automating cutting processes. In this paper, the feed cutting force estimated with the aid of an inexpensive current sensor installed on the ac servomotor of a(More)
—For pearls and other smooth alike lustrous jewels, the apparent shininess is one of the most important factors of beauty. This paper proposes an approach to automatic assessment of spherical surface quality in measure of shininess and smoothness using artificial vision. It traces a light ray emitted by a point source and images the resulting highlight(More)
In manufacturing systems such as ¯exible manufacturing systems (FMS), one of the most important issues is accurate detection of the tool conditions under given cutting conditions. An investigation is presented of a tool condition monitoring system (TCMS), which consists of a wavelet transform preprocessor for generating features from acoustic emission (AE)(More)
In this paper, we formulate visual tracking as random walks on graph models with nodes representing superpixels and edges denoting relationships between superpixels. We integrate two novel graphs with the theory of Markov random walks, resulting in two Markov chains. First, an ergodic Markov chain is enforced to globally search for the candidate nodes with(More)
In this paper, we propose a novel tracking framework based on discriminative supervised hashing algorithm. Different from previous methods, we treat tracking as a problem of object matching in a binary space. Using the hash functions, all target templates and candidates are mapped into compact binary codes, with which the target matching is conducted(More)
Incremental clustering approaches have been proposed for handling large data when given data set is too large to be stored. The key idea of these approaches is to find representatives to represent each cluster in each data chunk and final data analysis is carried out based on those identified representatives from all the chunks. However, most of the(More)
—In this paper, we present a novel way of pre-training deep architectures by using the stochastic least squares autoen-coder (SLSA). The SLSA is based on the combination of stochastic least squares estimation and logistic sampling. The usefulness of the stochastic least squares approach coupled with the numerical trick of constraining the logistic sampling(More)