We have implemented a method that detects planar regions from 3D scan data using Random Sample Consensus (RANSAC) algorithm to address the issue of a tradeoff between the scanning speed and the point density of 3D scanning. However, the limitation of the implemented method has not been clear yet. In this paper, we conducted an additional experiment to evaluate the implemented method by changing...

This study explores a semi-supervised classification approach using random forest as a base classifier to classify the low-back disorders (LBDs) risk associated with the industrial jobs. Semi-supervised classification approach uses unlabeled data together with the small number of labelled data to create a better classifier. The results obtained by the proposed approach are compared with those o...

A stochastic network model is developed which describes the 3D morphology of the pore space in fibre-based materials. It has the form of a random geometric graph, where the vertex set is modelled by random point processes and the edges are put using tools from graph theory and Markov chain Monte Carlo simulation. The model parameters are fitted to real image data gained by X-ray synchrotron tom...

A transition from surface-roughening collapse to a random network collapse has been investigated in fatty acid Langmuir monolayers. In contrast to the random network crack pattern, the surface-roughening crack pattern grows on a much longer time scale and on a much shorter length scale. A change in the isotherm from a surface pressure "spike" to a "plateau" occurs at the transition. In the surf...

In this paper, a fuzzy random minimum cost flow problem is presented. In this problem, cost parameters and decision variables are fuzzy random variables and fuzzy numbers respectively. The object of the problem is to find optimal flows of a capacitated network. Then, two algorithms are developed to solve the problem based on Er-expected value of fuzzy random variables and chance-constrained pro...

I develop several new models that attempt to reproduce the structure of two-dimensional granular materials, and compare them with experiment using diagnostics from network science. I discuss one model that treats granular networks as a perturbation of a close-packed hexagonal lattice, and two models that are modifications of the random geometric graph (RGG). One of these modified RGGs simulates...

Deep convolutional neural networks (CNN) have achieved great success. On the other hand, modeling structural information has been proved critical in many vision problems. It is of great interest to integrate them effectively. In a classical neural network, there is no message passing between neurons in the same layer. In this paper, we propose a CRF-CNN framework which can simultaneously model ...

Graphite oxide constitutes carbon network with oxygen atoms both on hexagonal arrangement and the edge sites. Structural and electronic properties for graphite-oxygen complexes have been explored using first-principles total-energy calculations within the local density approximation (LDA). Band structures and density of states for the propose carbon 3D models are reported. A finite energy gap and...