Fast isolation of faults in transmission systems using current transients

This paper presents a protection scheme that is capable of very fast isolation of faults in high voltage transmission systems. Proposed scheme comprises set of relays connected through a telecommunication network, located at different nodes of the system. Relays use wavelet coefficients of current signals to identify the fault directions relative to their location. Fault directions identified at different locations in the system can be combined to determine the faulted line (or busbar) and isolate it. A robust single ended traveling wave based fault distance estimation approach is proposed as a backup in case of communication failure. Investigations were carried out using time domain simulations in PSCAD/EMTDC for a high voltage transmission system.     

Strut-and-tie modelling of reinforced concrete beams using genetic algorithms optimization

This paper presents a method for automatically producing optimal strut-and-tie models for the design of reinforced concrete beams. The optimal model is generated by means of an optimization problem solved by using genetic algorithms. The basic idea developed here is that from an initial random generation of possible configurations of the strut-and-tie model for the beam subjected to study, new populations of possible configurations may be generated in an iterative way by using genetic operators until reaching an optimum solution for the studied problem which corresponds to the strut-and-tie configuration which minimizes the total strain energy. In the optimal configuration, compressive struts are not enforced to be parallel, which allows representing more consistently the physical reality of the flow of forces. Furthermore, the method is more simple and easier to apply than the methods based on the concepts of evolutionary structural optimization.     

Superhydrophobic surfaces with silane‐treated diatomaceous earth/resin systems

Superhydrophobic coatings were prepared using fluorosilane‐treated diatomaceous earth (DE) with either polyurethane or epoxy binders. The surface wettability and morphology of the films were analyzed using contact angle measurements and scanning electron microscopy (SEM), respectively. The water contact angles were studied as a function of the fluorocarbon fraction on DE and the particle loadings of treated DE in the coating. The contact angles exceeded 150° for coatings with at least 0.02 fluorocarbon fraction (mass of fluorosilane/mass of particle) on the DE and with 0.2 particle loadings (mass of treated particles/mass of coating). The water contact angles of the surfaces were dependent on the nature of the binder below 0.2 particle loadings of the superhydrophobic DE particles, but were independent of the binder type after attaining superhydrophobicity. The results were consistent with the superhydrophobicity resulting from the migration of the superhydrophobic DE moving to and covering the surfaces completely. It was also shown that the treatment with fluorosilanes restricted the pores in DE and reduces the specific surface area of the material. However, these changes had effectively no effect on the superhydrophobicity of the coatings. The results of this work clearly identify some important considerations relative to producing superhydrophobic coatings from inexpensive diatomaceous earth. © 2016 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2016 , 133, 44072.     

Use of artificial neural network to evaluate the vibration mitigation performance of geofoam-filled trenches

Development activities in a city often generate ground vibration that can cause discomfort to the occupants in nearby buildings, disturbances to the activities undertaken in the buildings and possible damage to nearby structures. This ground vibration is caused by construction activities such as pile driving, ground compaction etc., and road and rail traffic. The use of trenches has been an effective way to mitigate the adverse effects of such ground vibration. The effectiveness of the trench depends on many parameters including the properties of the vibration source, soil medium and trench in-fill material, trench dimensions and the requirements of the receiver. The process of selecting an effective trench for vibration mitigation can therefore become complex due to the influence of a number of parameters and their wide range of values. This paper investigates the use of artificial neural network (ANN) as a smart and efficient tool to predict the effectiveness of geofoam-filled trenches to mitigate ground vibration. Towards this end, a database is developed from an extensive study on the effects of the controlling parameters through numerical simulations with a validated finite element (FE) model. At a certain distance from the vibration source, a geofoam-filled trench is introduced to evaluate the efficiency of vibration mitigation with changes in key parameters such as excitation frequency, amplitude of load, trench configuration (i.e. depth and width), soil shear wave velocity, soil density and damping ratio. These were selected as the input parameters for the ANN while amplitude reduction ratio and peak particle velocity (PPV) were considered as outputs. A multilayer feed forward network was used and trained with the Levenberg-Marquardt algorithm. Neural networks with different configurations were evaluated by comparing coefficient of determination (R²) and mean square error (MSE). The optimum architecture was then used to predict previous results, which revealed the accuracy and the effectiveness of the ANN approach. The findings of this study will provide useful information for vibration mitigation using geofoam-filed trenches.     

Production of bioactive proteins and peptides from the diatom Nitzschia laevis and comparison of their in vitro antioxidant activities with those from Spirulina platensis and Chlorella vulgaris

This research focuses on green production of bioactive proteins and hydrolysates from Nitzschia. A comparison of antioxidant activities was established between protein extracts and hydrolysates from Nitzschia and two other well‐known microalgae, chlorella and spirulina. Protein hydrolysates from these microalgae were produced using Alcalase^®, Flavourzyme^® and Trypsin. The hydrolysis process enhanced the antioxidant activities in general, especially those obtained using Alcalase^®. Nitzschia showed the highest (P < 0.05) total phenolic content/reducing capacity (2.4 ± 0.02 mg GAE/100 g) after 90 min of hydrolysis with Alcalase^®. The ABTS [2,2′‐Azino‐bis(3‐ethylbenzothiazoline‐6‐sulphonic acid)] radical scavenging activity (66.77 ± 0.00%) was highest (P < 0.05) after 120 min of hydrolysis, but DPPH (2,2‐Diphenyl‐1‐picrylhydrazyl radical) was low (29.59 ± 0.02%). A correlation between ABTS activity and total phenolic contents was the highest (P < 0.05) for protein hydrolysates from all three organisms using Alcalase^®, but superoxide anion radical scavenging activity was intermediate for Nitzschia. Therefore, Nitzschia protein hydrolysates have the potential to be used as antioxidants.     

Attentional processes in stereotype formation: A common model for category accentuation and illusory correlation.

Stereotype formation may be based on the exaggeration of real group differences (category accentuation) or the misperception of group differences that do not exist (illusory correlation). This research sought to account for both phenomena with J. K. Kruschke's (1996, 2001, 2003) attention theory of category learning. According to the model, the features of majority groups are learned earlier than the features of minority groups. In turn, the features that become associated with a minority are those that most distinguish it from the majority. This second process is driven by an attention-shifting mechanism that directs attention toward group-attribute pairings that facilitate differentiation of the two groups and may lead to the formation of stronger minority stereotypes. Five experiments supported this model as a common account for category accentuation and distinctiveness-based illusory correlation. Implications for the natures of stereotype formation, illusory correlation, and impression formation are discussed. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Toward multidimensional assignment data association in robot localization and mapping

It is well accepted that the data association or the correspondence problem is one of the toughest problems faced by any state estimation algorithm. Particularly in robotics, it is not very well addressed. This paper introduces a multidimensional assignment (MDA)-based data association algorithm for the simultaneous localization and map building (SLAM) problem in mobile robot navigation. The data association problem is cast in a general discrete optimization framework and the MDA formulation for multitarget tracking is extended for SLAM using sensor location uncertainty with the joint likelihood of measurements over multiple frames as the objective function. Methods for feature initialization and management are also integrated into the algorithm. When clutter is high and features are sparse, the compatibility information of features of a single measurement frame is not sufficient to make effective data-association decisions,thus compromising performance of single-frame-based methods. However, in a multiple-measurement-frame approach, the availability of more than one frame of measurement provides for more effective data-association decisions to be made, as consistency of measurements are looked at in several frames of measurement. Simulations are conducted to verify the performance gains over the conventional nearest neighbor (NN) data association algorithm and the joint compatibility branch and bound (JCBB) algorithm, especially in the presence of varying densities of spurious measurements and dynamic objects. Experimental results with ground truth are presented to demonstrate the practicality of the proposed data-association method in complex and large outdoor environments and its effectiveness over single-frame-based NN and JCBB schemes.     

Effective bandwidth estimation and testing for Markov sources

This work addresses the resource sharing problem in broadband communication networks that can guarantee some quality of service (QoS), and develops some results about data source and traffic modelling, especially in aspects of model testing and parameter estimation. The multiplexing of variable bit rate (VBR) sources poses a mathematical and statistical problem: the estimation of the resource requirements of a source or set of sources. The estimation method shall be simple enough to be practically implemented in the connection acceptance control (CAC) function.

In this paper, the VBR video sources are taken as a typical case of variable rate, with real-time constraints. This association of requirements makes the case especially interesting. A Markov model is assumed for the VBR sources. The validity of such models is under research; they seem to be appropriate at least in certain time scales. The model is tested against real video traces. In order to estimate the resource allocation or “channel occupation” of each source, the concept of equivalent bandwidth proposed by Kelly [Notes on effective bandwidth, in: F.P. Kelly, S. Zachary, I.B. Ziedins (Eds.), Stochastic Networks: Theory and Applications, Oxford University Press, Oxford, 1996, pp. 141] is used; it is based on a consistent mathematical theory, and has proven to be robust and useful for technical applications.

A calculation of the equivalent bandwidth of a Markov source, given its parameters, can be found in the literature [IEEE ACM Trans. Networking 1 (4) (1993) 424]. But in fact, one can only estimate model and parameters. In this work, an estimation of the equivalent bandwidth is given, which can be obtained from real data. The convergence and the consistency of the estimation are studied, and practical bounds are found. Illustrative calculations are performed from real video traces that were obtained using a software MPEG coder, developed by the authors. The mathematical and statistical results are valid for whatever phenomenon that can be modelled as a Markov process.     

Evaluating the function of hippocampal subregions with high-resolution MRI in Alzheimer's disease and aging

Memory ability declines in older age groups. There is a growing list of physiological processes that target the hippocampal formation in an age-related fashion, and some might underlie the hippocampal component of memory decline. The hippocampal formation is comprised of separate subregions, and physiological processes differentially target these subregions. The ability to evaluate the functional integrity of individual subregions-performing subregional analysis-is a major clinical goal since it can aid in the diagnosis of memory decline, as well as in elucidating mechanisms of disease and testing potential interventions. Because of its superior spatial resolution, magnetic resonance imaging (MRI) is best suited to accomplish this goal. Despite limited success, most functional MRI (fMRI) protocols have difficulty in performing complete subregional analysis of the hippocampal formation. Here we address sources of difficulty by (1) generating T2* -weighted maps of the hippocampal formation with sub-millimeter resolution; and (2) by adapting an approach used by animal investigators to identify the hippocampal subregions using anatomical landmarks. The protocol is tested in patients with Alzheimer's disease and in healthy controls, in an effort to determine whether it can detect neuronal dysfunction. Results showed diminished signal in the hippocampal formation of patients with Alzheimer's disease (AD) compared to controls, and multivariate analysis showed that this difference was most prominent in the entorhinal cortex. The protocol can be used to perform subregional analysis of the hippocampal formation. Testing the protocol in other clinical populations is needed to demonstrate its efficacy in evaluating the neuronal integrity of all hippocampal subregions.