A new methodology for optimization and prediction of rate of penetration during drilling operations

Predictive models have been widely used in different engineering fields, as well as in petroleum engineering. Due to the development of high-performance computer systems, the accuracy and complexity of predictive models have been increased significantly. One of the common methods for prediction is artificial neural network (ANN). ANN models in combination with optimization algorithms provide a powerful and fast tool for the prediction and optimization of processes which take a large amount of time if they are simulated using common simulation technics. In the present paper, to predict penetration rate during drilling process, several ANN models were developed based on the data obtained from drilling of a gas well located in south of Iran. Regarding the R2 and RMSE values of the developed models, the best model was selected for prediction of penetration rate. In the next step, artificial bee colony algorithm was used for optimization of the parameters which are effective on rate of penetration (ROP). Results showed that the model is accurate enough for being used in the prediction and optimization of ROP in drilling operations.     

Benders Decomposition Algorithms for Two Variants of the Single Allocation Hub Location Problem

Networks and Spatial Economics - The hub location problem (HLP) is a special type of the facility location problem with numerous applications in the airline industry, postal services, and computer...     

Cooperative actuator fault accommodation in formation flight of unmanned vehicles using relative measurements

In this article, the cooperative fault accommodation in formation flight of unmanned vehicles is investigated through a hierarchical framework. Three levels are envisaged, namely a low-level fault recovery (LLFR), a formation-level fault recovery (FLFR) and a high-level (HL). In the LLFR module, a recovery controller is designed by using an estimate of the actuator fault. A performance monitoring module is introduced at the HL hierarchy to identify a partially low-level (LL) recovered vehicle due to inaccuracy in the fault estimate which results in violating the error specification of the formation mission. The HL supervisor then activates the FLFR module to compensate for the performance degradations of the partially LL recovered vehicle at the expense of the other healthy vehicles. Both centralised and decentralised control approaches are developed for our proposed cooperative fault recovery technique. A robust H _∞ controller is designed in which the parameters of the controller are adjusted to accommodate for the partially LL-recovered vehicle by enforcing that the other healthy vehicles allocate more control effort to compensate for the performance degradations of the faulty vehicle. Numerical simulations for a formation flight of five satellites are provided in the deep space, which do indeed confirm the validity and effectiveness of our proposed analytical work.     

A theory of mode of action of azolylalkylquinolines as DNA binding agents using automated flexible ligand docking

Azolylalkylquinolines (AAQs) are a family of quinolines with varying degrees of cytotoxic activity (comparable or moderately superior to adriamycin in some cases) developed in the past decade in our group where their exact mode of action is still unclear. In this study the most probable DNA binding mode of AAQs was investigated employing a novel flexible ligand docking approach by using AutoDock 3.0. Forty-nine AAQs with known experimental inhibitory activity were docked onto d(CGCAAATTTGCG)(2), d(CGATCG)(2) and d(CGCG)(2) oligonucleotides retrieved from the Protein Data Bank (PDB IDs: 102D, 1D12 and 1D32, respectively) as the representatives of the three plausible models of interactions between chemotherapeutic agents and DNA (groove binding, groove binding plus intercalation and bisintercalation, respectively). Good correlation (r(2)=0.64) between calculated binding energies and experimental inhibitory activities was obtained using groove binding plus intercalation model for phenyl-azolylalkylquinoline (PAAQ) series. Our findings show that the most probable mode of action of PAAQs as DNA binding agents is via intercalation of quinolinic moiety between CG base pairs with linker chain and azole moiety binding to the minor groove.     

Nonlinear control techniques for an atomic force microscope system

Two nonlinear control techniques are proposed for an atomic force microscope system. Initially, a learning-based control algorithm is developed for the microcantilever-sample system that achieves asymptotic cantilever tip tracking for periodic trajectories. Specifically, the control approach utilizes a learning-based feedforward term to compensate for periodic dynamics and high-gain terms to account for non-periodic dynamics. An adaptive control algorithm is then developed to achieve asymptotic cantilever tip tracking for bounded tip trajectories despite uncertainty throughout the system parameters. Simulation results are provided to illttstrate the efficacy and performance of the control strategies.     

Adaptive multi‐scale analysis for point‐based surface editing

This paper presents a tool that enables the direct editing of surface features in large point‐clouds or meshes. This is made possible by a novel multi‐scale analysis of unstructured point‐clouds that automatically extracts the number of relevant features together with their respective scale all over the surface. Then, combining this ingredient with an adequate multi‐scale decomposition allows us to directly enhance or reduce each feature in an independent manner. Our feature extraction is based on the analysis of the scale‐variations of locally fitted surface primitives combined with unsupervised learning techniques. Our tool may be applied either globally or locally, and millions of points are handled in real‐time. The resulting system enables users to accurately edit complex geometries with minimal interaction.     

Context-aware saliency detection for image retargeting using convolutional neural networks

Multimedia Tools and Applications - Image retargeting is the task of making images capable of being displayed on screens with different sizes. This work should be done so that high-level visual...     

Enhancing encoding capacity of combinational test stimulus decompressors

While scan-based compression is widely utilized in order to alleviate the test time and data volume problems,the overall compression level is dictated not only by the chain to channel ratio but also the ratio of encodable patterns.Aggressively increasing the number of scan chains in an effort to raise the compression levels may reduce the ratio of encodable patterns,degrading the overall compression level.In this paper,we present various methods to improve the ratio of encodable patterns.These methods are b...     

A semi-parametric generalization of the Cox proportional hazards regression model: Inference and Applications

The assumption of proportional hazards (PH) fundamental to the Cox PH model sometimes may not hold in practice. In this paper, we propose a generalization of the Cox PH model in terms of the cumulative hazard function taking a form similar to the Cox PH model, with the extension that the baseline cumulative hazard function is raised to a power function. Our model allows for interaction between covariates and the baseline hazard and it also includes, for the two sample problem, the case of two Weibull distributions and two extreme value distributions differing in both scale and shape parameters. The partial likelihood approach can not be applied here to estimate the model parameters. We use the full likelihood approach via a cubic B-spline approximation for the baseline hazard to estimate the model parameters. A semi-automatic procedure for knot selection based on Akaike’s information criterion is developed. We illustrate the applicability of our approach using real-life data.