Potts Models with (17) Invisible States on Thin Graphs

The order of a phase transition is usually determined by the nature of the symmetry breaking at the phase transition point and the dimension of the model under consideration. For instance, q-state Potts models in two dimensions display a second order, continuous transition for q = 2,3,4 and first order for higher q. Tamura et al recently introduced Potts models with "invisible" states which contribute to the entropy but not the internal energy and noted that adding such invisible states could transmute continuous transitions into first order transitions. This was observed both in a Bragg-Williams type mean-field calculation and 2D Monte-Carlo simulations. It was suggested that the invisible state mechanism for transmuting the order of a transition might play a role where transition orders inconsistent with the usual scheme had been observed. In this paper we note that an alternative mean-field approach employing 3-regular random ("thin") graphs also displays this change in the order of the transition as the number of invisible states is varied, although the number of states required to effect the transmutation, 17 invisible states when there are 2 visible states, is much higher than in the Bragg-Williams case. The calculation proceeds by using the equivalence of the Potts model with 2 visible and r invisible states to the Blume-Emery-Griffiths (BEG) model, so a by-product is the solution of the BEG model on thin random graphs.Comment: (2) Minor typos corrected, references update

Application of artificial intelligence techniques for rolling dynamic compaction

Rolling dynamic compaction (RDC), involving non-circular modules towed behind a tractor, is now widespread and accepted among many other soil compaction methods. However, to date, there is no accurate method to reliably predict the increase in soil strength after the application of a given number of passes of RDC. This paper presents the application of artificial intelligence (AI) techniques in the form of artificial neural networks (ANNs) and genetic programming (GP) for a priori prediction of the density improvement by means of RDC in a range of ground conditions. These AI-based models are developed by using in situ soil test data, specifically cone penetration test (CPT) and dynamic cone penetration (DCP) test data obtained from several ground improvement projects that employed the 4- sided, 8-tonne ‘impact roller’. The predictions of ANN- and GP-based models are compared with the corresponding actual values and they show strong correlations (r > 0.8). Additionally, the robustness of the optimal models is investigated in a parametric study and it is observed that the model predictions are in a good agreement with the expected behaviour of RDC.R. A. T. M. Ranasinghe and M. B. Jaks

Spatial prediction in mobile robotic wireless sensor networks with network constraints

© 2016 IEEE. In recent years mobile robotic wireless sensor networks have been a popular choice for modelling spatial phenomena. This research is highly demanding and non-trivial due to challenges from both network and robotic aspects. In this paper, we address the spatial modelling of a physical phenomena with the network connectivity constraints while the mobile robots are striving to achieve the minimum modelling mismatch in terms of root mean square error (RMSE). We have resolved it through Gauss markov random field based approach which is a computationally efficient implementation of Gaussian processes. In this strategy, the Mobile Robotic Wireless Sensor Node (MRWSN) are centrally controlled to maintain the connectivity while minimizing the RMSE. Once the number of MRWSNs reach their maximum coverage, a new MRWSN is requested at the most informative location. The experimental results are convincing and they show the effectiveness of the algorithm

Road terrain type classification based on laser measurement system data

For road vehicles, knowledge of terrain types is useful in improving passenger safety and comfort. The conventional methods are susceptible to vehicle speed variations and in this paper we present a method of using Laser Measurement System (LMS) data for speed independent road type classification. Experiments were carried out with an instrumented road vehicle (CRUISE), by manually driving on a variety of road terrain types namely Asphalt, Concrete, Grass, and Gravel roads at different speeds. A looking down LMS is used for capturing the terrain data. The range data is capable of capturing the structural differences while the remission values are used to observe anomalies in surface reflectance properties. Both measurements are combined and used in a Support Vector Machines Classifier to achieve an average accuracy of 95% on different road types

Fast indoor scene classification using 3D point clouds

A representation of space that includes both geometric and semantic information enables a robot to perform high-level tasks in complex environments. Identifying and categorizing environments based on onboard sensors are essential in these scenarios. The Kinect™, a 3D low cost sensor is appealing in these scenarios as it can provide rich information. The downside is the presence of large amount of information, which could lead to higher computational complexity. In this paper, we propose a methodology to efficiently classify indoor environments into semantic categories using Kinect™ data. With a fast feature extraction method along with an efficient feature selection algorithm (DEFS) and, support vector machines (SVM) classifier, we could realize a fast scene classification algorithm. Experimental results in an indoor scenario are presented including comparisons with its counterpart of commonly available 2D laser range finder data

A highly accurate and scalable approach for addressing location uncertainty in asset tracking applications

Tracking systems that use RFID are increasingly being used for monitoring the movement of goods in supply chains. While these systems are effective, they still have to overcome significant challenges, such as missing reads, to improve their performance further. In this paper, we describe an optimised tracking algorithm to predict the locations of objects in the presence of missed reads using particle filters. To achieve high location accuracy we develop a model that characterises the motion of objects in a supply chain. The model is also adaptable to the changing nature of a business such as flow of goods, path taken by goods through the supply chain, and sales volumes. A scalable tracking algorithm is achieved by an object compression technique, which also leads to a significant improvement in accuracy. The results of a detailed simulation study shows that our object compression technique yields high location accuracy (above 98% at 0.95 read rate) with significant reductions in execution time and memory usage.Rengamathi Sankarkumar, Damith C. Ranasinghe, Thuraiappah Sathya