Fiber-optic liquid level sensor based on coupling optical path length variation

The concept for a new and simple fiber-optic liquid level sensor is presented and experimental results are shown to demonstrate the principle. The sensing principle is based on light intensity modulation when rising and falling mode of liquid level causes coupling optical path distance variation between two optical fibers. Near continuous mode of liquid level variation could be monitored with resolution as low as 1 mm can be measured in the length scale of 25 cm.     

Rough Self Organizing Map

A rough self-organizing map (RSOM) with fuzzy discretization of feature space is described here. Discernibility reducts obtained using rough set theory are used to extract domain knowledge in an unsupervised framework. Reducts are then used to determine the initial weights of the network, which are further refined using competitive learning. Superiority of this network in terms of quality of clusters, learning time and representation of data is demonstrated quantitatively through experiments over the conventional SOM.     

Robust nonlinear analytic redundancy for fault detection and isolation in mobile robot

A robust nonlinear analytical redundancy (RNLAR) technique is presented to detect and isolate actuator and sensor faults in a mobile robot. Both model-plant-mismatch (MPM) and process disturbance are considered during fault detection. The RNLAR is used to design primary residual vectors (PRV), which are highly sensitive to the faults and less sensitive to MPM and process disturbance, for sensor and actuator fault detection. The PRVs are then transformed into a set of structured residual vectors (SRV) for fault isolation. Experimental results on a Pioneer 3-DX mobile robot are presented to justify the effectiveness of the RNLAR scheme.     

Improved mathematical representation of arc static characteristic

In this letter, the importance of a satisfactory voltage/current relationship in the analytical studies of the electric arc is stressed, and an improved empirical relation for static characteristic of an arc is proposed.     

A probabilistic active support vector learning algorithm

The paper describes a probabilistic active learning strategy for support vector machine (SVM) design in large data applications. The learning strategy is motivated by the statistical query model. While most existing methods of active SVM learning query for points based on their proximity to the current separating hyperplane, the proposed method queries for a set of points according to a distribution as determined by the current separating hyperplane and a newly defined concept of an adaptive confidence factor. This enables the algorithm to have more robust and efficient learning capabilities. The confidence factor is estimated from local information using the k nearest neighbor principle. The effectiveness of the method is demonstrated on real-life data sets both in terms of generalization performance, query complexity, and training time.     

Enantioselective hydrogenation of pyruvate esters in the mesoporous environment of Pt-MCM-41

Pt-MCM-41 catalysts having loadings of up to 2% Pt have been synthesised using three strategies: (i) direct synthesis from a Pt²⁺-containing gel, (ii) exchange of Na⁺ in Al-MCM-41 (containing 8% aluminium) for Pt²⁺, (iii) exchange of H⁺ in H-MCM-41 (containing 1% aluminium) for Pt²⁺. HRTEM confirmed the retention of the mesoporous structure in the active catalysts and gave information on Pt particle size and location. ²⁷Al NMR provided information on the movement of aluminium within the structure during catalyst preparation. Enantioselective hydrogenation of methyl and ethyl pyruvate was catalysed by cinchonidine-modified Pt-MCM-41 at 293 K and elevated hydrogen pressures; performance was compared to that provided by the standard reference catalyst EUROPT-1. Catalysts prepared by strategies (i) and (ii) performed best, giving values of the enantiomeric excess comparable to those afforded by EUROPT-1 at rates moderated by an order of magnitude by mass transfer effects. Performance was impaired when Pt particle size in the mesopores was so small that conditions favoured racemic reaction. This revised version was published online in August 2006 with corrections to the Cover Date.     

Torque and efficiency maximization for a wave energy harvesting turbine: an approach to modify multiple design variables

Multiple design variables modifications are carried out for a bidirectional flow turbine used in an oscillating water column wave energy converter to enhance its performance by maximizing the peak torque‐coefficient (TC) and the corresponding efficiency (EFF), which are the objective functions of this problem. The Latin hypercube sampling technique selects samples from a designed space created by the design parameters defined for the blade sweep and aerofoil profile thickness modifications. The objective function values are obtained by solving Reynolds‐averaged Navier–Stokes equations and are approximated by surrogate models. The models help in generating populations of the genetic algorithm, which finally produces a set of optimal designs in a Pareto optimal front. Only two extreme designs among the five clustered points are further evaluated by solving Reynolds‐averaged Navier–Stokes equations to cross‐check the validity of the optimization steps. It is found that the TC is increased by 33% and the EFF is decreased by 5% at one extreme cluster point, while the other extreme point gives that both the TC and the EFF are higher by 1.8% and 2.9%, respectively, as compared with the reference geometry. The optimal geometry has a wider operating range, which is an important parameter to get continuous power from a wave energy converter. Copyright © 2016 John Wiley & Sons, Ltd.     

Mobile beacon based range free localization method for wireless sensor networks

Wireless Networks - The existing mobile beacon based range free localization method has two basic problems: (1) the accuracy of position estimation depends on closer beacon broadcasting intervals...     

Identification of Candidate Genes for Root Traits Using Genotype–Phenotype Association Analysis of Near-Isogenic Lines in Hexaploid Wheat (Triticum aestivum L.)

Global wheat (Triticum aestivum L.) production is constrained by different biotic and abiotic stresses, which are increasing with climate change. An improved root system is essential for adaptability and sustainable wheat production. In this study, 10 pairs of near-isogenic lines (NILs)—targeting four genomic regions (GRs) on chromosome arms 4BS, 4BL, 4AS, and 7AL of hexaploid wheat—were used to phenotype root traits in a semi-hydroponic system. Seven of the 10 NIL pairs significantly differed between their isolines for 11 root traits. The NIL pairs targeting qDSI.4B.1 GR varied the most, followed by the NIL pair targeting qDT.4A.1 and QHtscc.ksu-7A GRs. For pairs 5–7 targeting qDT.4A.1 GR, pair 6 significantly differed in the most root traits. Of the 4 NIL pairs targeting qDSI.4B.1 GR, pairs 2 and 4 significantly differed in 3 and 4 root traits, respectively. Pairs 9 and 10 targeting QHtscc.ksu-7A GR significantly differed in 1 and 4 root traits, respectively. Using the wheat 90K Illumina iSelect array, we identified 15 putative candidate genes associated with different root traits in the contrasting isolines, in which two UDP-glycosyltransferase (UGT)-encoding genes, TraesCS4A02G185300 and TraesCS4A02G442700, and a leucine-rich repeat receptor-like protein kinase (LRR-RLK)-encoding gene, TraesCS4A02G330900, also showed important functions for root trait control in other crops. This study characterized, for the first time, that these GRs control root traits in wheat, and identified candidate genes, although the candidate genes will need further confirmation and validation for marker-assisted wheat breeding.