Influence of cement grouts composition on the rheological behaviour

The influence of water to cement ratio, HRWRA content and viscosity modifying admixture content on the rheological characteristics of cement grouts considered as Herschel–Bulkley fluids is studied experimentally. Results show that cement grouts without chemical admixtures and cement grouts containing only a viscosity modifying admixture present a shear-thinning behaviour with an approximately constant value for the exponent n of Herschel–Bulkley model. On the contrary, grouts containing a HRWRA content near the saturation point exhibit quasi Binghamian behaviour. The Herschel–Bulkley model describes properly the rheological behaviour of cement grouts without chemical admixtures. It can be applied correctly to grouts containing HRWRA and/or viscosity modifying admixture in a shear rate range comprised between 4 s^{− 1} and 100 s^{− 1} and can be used to predict satisfactorily the Marsh cone flow time of cement grouts of widely varying compositions.     

Enhancing the accuracy of rainfall-induced landslide prediction along mountain roads with a GIS-based random forest classifier

Along mountain roads, rainfall-triggered landslides are typical disasters that cause significant human casualties. Thus, to establish effective mitigation measures, it would be very useful were government agencies and practicing land-use planners to have the capability to make an accurate landslide evaluation. Here, we propose a machine learning methodology for the spatial prediction of rainfall-induced landslides along mountain roads which is based on a random forest classifier (RFC) and a GIS-based dataset. The RFC is used as a supervised learning technique to generalize the classification boundary that separates the input information of ten landslide conditioning factors (slope, aspect, relief amplitude, toposhape, topographic wetness index, distance to roads, distance to rivers, lithology, distance to faults, and rainfall) into two distinctive class labels: ‘landslide’ and ‘non-landslide’. Experimental results with a cross validation process and sensitivity analysis on the RFC model parameters reveal that the proposed model achieves a superior prediction accuracy with an area under the curve  of 0.92. The RFC significantly outperforms other benchmarking methods, including discriminant analysis, logistic regression, artificial neural networks, relevance vector machines, and support vector machines. Based on our experimental outcome and comparative analysis, we strongly recommend the RFC as a very capable tool for spatial modeling of rainfall-induced landslides.

     

Comparative Study of Streamers＇ Characteristics in Different Seed Based Insulating Oils under Lightning Impulse Voltages

This paper is aimed at the streamers in natural esters （vegetable oils） in point--plane electrode arrangement under lightning impulse voltage. The shape, stopping length, velocity and current of streamers are investigated. Six untreated commercial oils extracted from grape seeds, sunflower and rape seeds, corn, rice and sesame that could constitute potential liquids for high voltage applications are tested. A naphthenic mineral oil is also tested for comparison. It＇s shown that the streamers are filamentary for both polarities. For a given voltage, the stopping lengths （Lf） of streamers are longer when the point is positive than when it is negative; also, except mineral oil when the point is negative, the values of Lf-are very close in all tested oils. The streamers＇ velocities are in the same range for all vegetable oils and they vary between 0.4 km/s and 1.2 km/s for positive polarity and 0.2 km/s and 0.8 km/s for negative polarity.     

BA*: an online complete coverage algorithm for cleaning robots

This paper presents a novel approach to solve the online complete coverage task of autonomous cleaning robots in unknown workspaces based on the boustrophedon motions and the A* search algorithm (BA*). In this approach, the robot performs a single boustrophedon motion to cover an unvisited region until it reaches a critical point. To continue covering the next unvisited region, the robot wisely detects backtracking points based on its accumulated knowledge, determines the best backtracking point as the starting point of the next boustrophedon motion, and applies an intelligent backtracking mechanism based on the proposed A* search with smoothed path on tiling so as to reach the starting point with the shortest collision-free path. The robot achieves complete coverage when no backtracking point is detected. Computer simulations and experiments in real workspaces prove that our proposed BA* is efficient for the complete coverage task of cleaning robots.     

Monte-Carlo tree search for Bayesian reinforcement learning

Bayesian model-based reinforcement learning can be formulated as a partially observable Markov decision process (POMDP) to provide a principled framework for optimally balancing exploitation and exploration. Then, a POMDP solver can be used to solve the problem. If the prior distribution over the environment’s dynamics is a product of Dirichlet distributions, the POMDP’s optimal value function can be represented using a set of multivariate polynomials. Unfortunately, the size of the polynomials grows exponentially with the problem horizon. In this paper, we examine the use of an online Monte-Carlo tree search (MCTS) algorithm for large POMDPs, to solve the Bayesian reinforcement learning problem online. We will show that such an algorithm successfully searches for a near-optimal policy. In addition, we examine the use of a parameter tying method to keep the model search space small, and propose the use of nested mixture of tied models to increase robustness of the method when our prior information does not allow us to specify the structure of tied models exactly. Experiments show that the proposed methods substantially improve scalability of current Bayesian reinforcement learning methods.     

Batch-Theta* for path planning to the best goal in a goal set

The development of 3D cameras and many navigation-supporting sensors has recently enabled robots to build their working maps and navigate accurately, making path planning popular not just on computer graphics, but in real environments as well. Pursuing the solutions for robot path planning, this paper presents a variant of searching method Theta* for choosing the best goal among given goals and the lowest-cost path to it, called Batch-Theta*. The novelty lies at the proposed line-of-sight checking function during the searching process and the manner that the method handles the batch of goals during one search instead of repeatedly considering a single goal or blindly doing the exhausted search. The analysis and simulations show that the proposed Batch-Theta* efficiently finds the lowest-cost path to the best goal in a given goal set under Theta*’s mechanism.     

A distributed design for multiple moving source positioning

Acoustic source localization has many important applications particularly for military tracking foreign objects. Even though Wireless Sensor Networks (WSNs) have been developed, this localization problem remains a big challenge. A system for solving source localization must have the ability to deal with the problems of recorded convolved mixture signals while minimizing the high communication and computation cost. This paper introduces a distributed design for positioning multiple independent moving sources based on acoustic signals in which we focus on utilizing the relative information of magnitudes recorded at different sensors. The sensors perform preprocessing on the sensed data to capture the most important information before compressing and sending extracted data to the base. At the base, the data is uncompressed and the source locations are inferred via two clustering stages and an optimization method. Analysis and simulation results lead to the conclusion that our system provides good accuracy and needs neither much communication nor complex computation in a distributed manner. It works well when there exists high noise with Rayleigh multipath fading under Doppler effect and even when the number of independent sources is greater than the number of microphone sensors.     

Distributed Push-pull Estimation for node localization in wireless sensor networks

A great deal of research achievements on localization in wireless sensor networks (WSNs) has been obtained in recent years. Nevertheless, its interesting challenges in terms of cost-reduction, accuracy improvement, scalability, and distributed ability design have led to the development of a new algorithm, the Push-pull Estimation (PPE). In this algorithm, the differences between measurements and current calculated distances are modeled into forces, dragging the nodes close to their actual positions. Based on very few known-location sensors or beacons, PPE can pervasively estimate the coordinates of many unknown-location sensors. Each unknown-location sensor, with given pair-wise distances, could independently estimate its own position through remarkably uncomplicated calculations. Characteristics of the algorithm are examined through analyses and simulations to demonstrate that it has advantages over those of previous works in dealing with the above challenges.     

Partial feedback linearization control of a three-dimensional overhead crane

Based on partial feedback linearization, an improved nonlinear controller is analyzed and designed for the three-dimensional motion of an overhead crane. Three control inputs composed of bridge moving, trolley travelling, and cargo hoisting forces are used to drive five state variables consisting of bridge motion, trolley movement, cargo hoisting displacement, and two cargo swing angles. The control scheme is constituted by linearly combining two components that are separately obtained from the nonlinear feedback of actuated and un-actuated states. To verify the quality of the control process, both numerical simulation and experimental study are carried out. The proposed controller asymptotically stabilizes all system states.