Using the genetic algorithm to enhance nonnegative matrix factorization initialization

Nonnegative matrix factorization (NMF) algorithms have been utilized in a wide range of real applications; however, the performance of NMF is highly dependent on three factors including: (1) choosing a problem dependent cost function; (2) using an effective initialization method to start the updating procedure from a near‐optimal point; and (3) determining the rank of factorized matrices prior to decomposition. Due to the nonconvex nature of the NMF cost function, finding an analytical‐based optimal solution is impossible. This paper is aimed at proposing an efficient initialization method to modify the NMF performance. To widely explore the search space for initializing the factorized matrices in NMF, the island genetic algorithm (IGA) is employed as a diverse multiagent search scheme. To adapt IGA for NMF initialization, we present a specific mutation operator. To assess how the proposed IGA initialization method efficiently enhances NMF performance, we have implemented state‐of‐the‐art initialization methods and applied to the Japanese Female Facial Expression dataset to recognize the facial expression states. Experimental results demonstrate the superiority of the proposed approach to the compared methods in terms of relative error and fast convergence.     

Flow characteristics of hyperboloid stirrers

The flows in a fully-baffled vessel with a diameter T = 144 mm driven by hyperboloid stirrers of diameters D = 773 and 27/3 have been visualised and characterised by local measurements of velocity and turbulence and by power number. The results were obtained for a range of rotational speeds from 6 to 40 rev/s. The visualisation showed that the larger stirrer gave rise to a radial jet and that the smaller stirrer formed a jet inclined towards the base of the vessel so that there was a tendency for the system of two vortices, one above and one below the jet, to give way to a single vortex as the clearance between the stirrer and the base of the vessel was reduced. The velocity measurements revealed bulk-flow values an order of magnitude less than that of the maximum radial velocity in the jet, that the maximum radial velocity was 24% of the circumferential velocity of the tip of the stirrer, and that the radial velocities were proportional to the rotational speed. The flows generated by the hyperboloid stirrer were less vigorous than those of Rushton impellers of similar radius and were associated with power numbers 28 times less. The power number did not vary with rotational speed or with clearance within the measured range. The contrast with propeller and disc stirrers is less pronounced, but the hyperbolic profile is likely to find application and the present results provide a basis for choice.     

Application of Taguchi method in determining optimum level of curing system of NBR/PVC blend

Taguchi method is applied in this study to determine the optimum level of curing system (sulfur, MBTS, and CBS) in a NBR/PVC blend. By considering physicomechanical properties of cured NBR/PVC blend, optimum level of curing system is determined. A fixed master batch formulation of NBR/PVC is used, and the effect of curing system is studied on the physicomechanical properties of NBR/PVC blend, such as tensile strength (TS) and elongation‐at‐break (EB) before and after aging and also hardness and abrasion. The L9 orthogonal array that includes nine rows and four columns is applied. In this matrix, rows show the experiments and three columns show the amount of three factors (sulfur, MBTS, and CBS) and one column is left arbitrarily as an empty column. In this array, the columns are mutually orthogonal. The optimum physicomechanical properties of cured NBR/PVC blend are at 2 phr sulfur, 2 phr MBTS, and 0.5 phr CBS of curing system. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 102: 5358–5362, 2006     

Upstream Cutoff and Downstream Filters to Control of Seepage in Dams

The finite element method was used in this study to investigate cutoff walls and downstream filters to control seepage, the exit hydraulic gradient, and uplift forces for dams. Experimental data was used for validating the numerical modelling. The effective parameters are the length of filter and its distance downstream from the dam, the depth of the cutoff walls, the upstream dam head, and the thickness of alluvial foundation. The results show that by increasing filter length, the exit hydraulic gradient, uplift force, and seepage are reduced. The optimum relative length of the filter is L/H?=?0.028 which results in a decrease of about 65% in the exit hydraulic gradient, a 35% decrease in seepage and 10% reduction in the uplift force at the upstream foundation and a 60% decrease in the uplift force at the downstream foundation. Increase of cutoff wall depth reduces the exit hydraulic gradient, uplift force, and seepage. Using two cutoff walls both upstream and downstream of the dam decreases seepage, hydraulic gradient, and uplift force 132%, 450% and 11% respectively. However, using an upstream cutoff and downstream filter decreases seepage, hydraulic gradient, and uplift force by 180%, 490%, and 119% respectively. Thus, based on this study, recommendations for suitable combinations of upstream cutoff and downstream filter are provided.

     

BASE: A bacteria foraging algorithm for cell formation with sequence data

A cellular manufacturing system (CMS) is considered an efficient production strategy for batch type production. A CMS relies on the principle of grouping machines into machine cells and grouping parts into part families on the basis of pertinent similarity measures. The bacteria foraging algorithm (BFA) is a newly developed computation technique extracted from the social foraging behavior of Escherichia coli (E. coli) bacteria. Ever since Kevin M. Passino invented the BFA, one of the main challenges has been employment of the algorithm to problem areas other than those for which the algorithm was proposed. This research work studies the first applications of this emerging novel optimization algorithm to the cell formation (CF) problem considering the operation sequence. In addition, a newly developed BFA-based optimization algorithm for CF based on operation sequences is discussed. In this paper, an attempt is made to solve the CF problem, while taking into consideration the number of voids in the cells and the number of inter-cell travels based on operational sequences of the parts visited by the machines. The BFA is suggested to create machine cells and part families. The performance of the proposed algorithm is compared with that of a number of algorithms that are most commonly used and reported in the corresponding scientific literature, such as the CASE clustering algorithm for sequence data, the ACCORD bicriterion clustering algorithm and modified ART1, and using a defined performance measure known as group technology efficiency and bond efficiency. The results show better performance of the proposed algorithm.     

Hybrid metaheuristics for scheduling of machines and transport robots in job shop environment

In real manufacturing environments, the control of some elements in systems based on robotic cells, such as transport robots has some difficulties when planning operations dynamically. The Job Shop scheduling Problem with Transportation times and Many Robots (JSPT-MR) is a generalization of the classical Job Shop scheduling Problem (JSP) where a set of jobs additionally have to be transported between machines by several transport robots. Hence, the JSPT-MR is more computationally difficult than the JSP presenting two NP-hard problems simultaneously: the job shop scheduling problem and the robot routing problem. This paper proposes a hybrid metaheuristic approach based on clustered holonic multiagent model for the JSPT-MR. Firstly, a scheduler agent applies a Neighborhood-based Genetic Algorithm (NGA) for a global exploration of the search space. Secondly, a set of cluster agents uses a tabu search technique to guide the research in promising regions. Computational results are presented using two sets of benchmark literature instances. New upper bounds are found, showing the effectiveness of the presented approach.     

High throughput blood plasma separation using a passive PMMA microfluidic device

Since plasma is rich in many biomarkers used in clinical diagnostic experiments, microscale blood plasma separation is a primitive step in most of microfluidic analytical chips. In this paper, a passive microfluidic device for on-chip blood plasma separation based on Zweifach–Fung effect and plasma skimming was designed and fabricated by hot embossing of microchannels on a PMMA substrate and thermal bonding process. Human blood was diluted in various times and injected into the device. The main novelty of the proposed microfluidic device is the design of diffuser-shaped daughter channels. Our results demonstrated that this design exerted a considerable positive influence on the separation efficiency of the passive separator device, and the separation efficiency of 66.6 % was achieved. The optimum purity efficiency of 70 % was achieved for 1:100 dilution times.     

Robust Stability and Stabilization Of TCP‐Networked Control Systems with Multiple Delay System Modeling

This paper presents a new model for networked control systems (NCSs) under transmission control protocol (TCP) as a multiple‐delay system by considering both sensor to controller and controller to actuator delays. An analytical TCP model has been considered for the network part, and an active queue management (AQM) controller is designed to regulate the desired queue length, which ensures holding the network induced delay and its variation within their lower bounds. The model is assumed to possess structured uncertainties due to the stochastic nature of the network. Robust stability and stabilization conditions are derived in terms of linear matrix inequalities (LMIs) by applying the Lyapunov‐Krasovskii stability criterion. Illustrative examples are presented and it has been shown that the proposed method will obtain less conservative results compared to the existing approaches in the literature.     

Selection of regression models for predicting strength and deformability properties of rocks using GA

Recently, many regression models have been presented for prediction of mechanical parameters of rocks regarding to rock index properties. Although statistical analysis is a common method for developing regression models, but still selection of suitable transformation of the independent variables in a regression model is difficult. In this paper, a genetic algorithm (GA) has been employed as a heuristic search method for selection of best transformation of the independent variables (some index properties of rocks) in regression models for prediction of uniaxial compressive strength (UCS) and modulus of elasticity (E). Firstly, multiple linear regression (MLR) analysis was performed on a data set to establish predictive models. Then, two GA models were developed in which root mean squared error (RMSE) was defined as fitness function. Results have shown that GA models are more precise than MLR models and are able to explain the relation between the intrinsic strength/elasticity properties and index properties of rocks by simple formulation and accepted accuracy.