Predator–prey optimization based clustering algorithm for wireless sensor networks

Neural Computing and Applications - Grouping the sensor nodes into clusters is an effective way to organize wireless sensor networks and to prolong the networks’ lifetime. This paper presents...     

Parallel matrix factorization for recommender systems

Matrix factorization, when the matrix has missing values, has become one of the leading techniques for recommender systems. To handle web-scale datasets with millions of users and billions of ratings, scalability becomes an important issue. Alternating least squares (ALS) and stochastic gradient descent (SGD) are two popular approaches to compute matrix factorization, and there has been a recent flurry of activity to parallelize these algorithms. However, due to the cubic time complexity in the target rank, ALS is not scalable to large-scale datasets. On the other hand, SGD conducts efficient updates but usually suffers from slow convergence that is sensitive to the parameters. Coordinate descent, a classical optimization approach, has been used for many other large-scale problems, but its application to matrix factorization for recommender systems has not been thoroughly explored. In this paper, we show that coordinate descent-based methods have a more efficient update rule compared to ALS and have faster and more stable convergence than SGD. We study different update sequences and propose the CCD++ algorithm, which updates rank-one factors one by one. In addition, CCD++ can be easily parallelized on both multi-core and distributed systems. We empirically show that CCD++ is much faster than ALS and SGD in both settings. As an example, with a synthetic dataset containing 14.6 billion ratings, on a distributed memory cluster with 64 processors, to deliver the desired test RMSE, CCD++ is 49 times faster than SGD and 20 times faster than ALS. When the number of processors is increased to 256, CCD++ takes only 16 s and is still 40 times faster than SGD and 20 times faster than ALS.     

Reliability Analysis Method on Repairable System with Standby Structure Based on Goal Oriented Methodology

This paper presents a reliability analysis method on repairable system with standby structure based on goal oriented (GO) methodology. Firstly, a new combination of GO operator, which is composed of a new logical GO operator named Type 18A operator and a new auxiliary GO operator named Type 20 operator, is created to represent standby mode. The availability formula of standby equipment with translation exception is deduced based on Markov process theory. Then, the application method of combination of GO operator for standby mode and the analysis process of repairable system with standby structure based on GO method are proposed. Thirdly, this new combination of GO operator is applied in availability analysis of the hydraulic oil supply system of power‐shift steering transmission. Finally, the results obtained by the new GO method are compared with the results of fault tree analysis, Monte Carlo simulation, GO methods using Type 2 operator and Type 18 operator to represent the standby mode, respectively. And the comparison results show that this new GO method is applicable and reasonable for reliability analysis of repairable system with standby structure. All in all, this paper provides guidance for reliability analysis of repairable systems with standby structure. Copyright © 2015 John Wiley & Sons, Ltd.     

Biotechnological potential of industrial wastes for economical citric acid bioproduction by Aspergillus niger through submerged fermentation

Submerged citric acid (CA) bioproduction was carried out by Aspergillus niger NRRL‐567 using various industrial wastes, such as brewery spent liquid (BSL), lactoserum and starch industry water sludge. CA bioproduction was carried out by varying the temperature (25–35 °C), pH (3–5), addition of inducers, incubation time and supplementation with different proportions of apple pomace ultrafiltration sludge (APS). The results indicated that under the best conditions with 3% (v/v) methanol, the optimal concentration of 11.34 g L^?1 CA was recorded using BSL at pH 3.5 and temperature 30 °C after 120‐h incubation period. Supplementation of methanol resulted in an increase of 56% CA production. Meanwhile, under similar conditions, higher concentration of 18.34 g L^?1 CA was reported with the supplementation of BSL with 40% (v/v) APS having suspended solids concentration of 30 g L^?1. The present study demonstrated the potential of BSL supplemented with APS as an alternative cheap substrate for CA fermentation.     

A sub-space method to detect multiple wireless microphone signals in TV band white space

The main hurdle in the realization of dynamic spectrum access systems from the physical layer perspective is the reliable sensing of low power licensed users. One such scenario shows up in the unlicensed use of the TV bands where the TV band devices are required to sense extremely low power wireless microphones (WMs). The lack of technical standards among various wireless manufacturers and the resemblance of certain WM signals to narrow-band interference signals, such as spurious emissions, further aggravate the problem. Due to these uncertainties, it is extremely difficult to abstract the features of WM signals and hence develop robust sensing algorithms. To partly counter these challenges, we develop a two-stage sub-space algorithm that detects multiple narrow-band analog frequency-modulated signals generated by WMs. The performance of the algorithm is verified by using the real WM signals experimentally captured under low SNR conditions. The problem of differentiating between the WM and other narrow-band signals is left as future work.     

Multiobjective optimal thermal power dispatch

A multiobjective thermal power dispatch problem is formulated using noncommensurable objectives such as operating costs and minimal emission. A sensitivity measure is chosen whereby the effects of variations in the nominal conditions describing a given multiobjective problem can be measured and incorporated as a performance index to be minimized. A nonlinear programming problem provides the framework for examining the objective constraint level in an ε-constant form of the multiobjective optimization problem. The dispersion index is chosen as the sensitivity measure for the investigation of the effects of random variations in the model parameters of the optimal solution. A sensitivity trade-off is exploited for the multiobjective problem that represents the trade-off between sensitivity and objective level. Validity of the method has been demonstrated by analysing a three-generator sample system.     

Transit vehicle reliability models

This paper presents two mathematical Markov models of repairable transit systems. Laplace transforms of the state probability equations are developed.     

Network reliability evaluation: Application of bathtub failure rate curve

This paper presents reliability evaluation of networks composed of two and three-state devices whose failure rate follows a bathtub curve. Networks reliability equations and (time dependent) curves are presented. Each network reliability time dependent curve is also plotted for the device constant failure rate.