A hybrid multi-objective shuffled frog-leaping algorithm for a mixed-model assembly line sequencing problem

In this paper, a mixed-model assembly line (MMAL) sequencing problem is studied. This type of production system is used to manufacture multiple products along a single assembly line while maintaining the least possible inventories. With the growth in customers’ demand diversification, mixed-model assembly lines have gained increasing importance in the field of management. Among the available criteria used to judge a sequence in MMAL, the following three are taken into account: the minimization of total utility work, total production rate variation, and total setup cost. Due to the complexity of the problem, it is very difficult to obtain optimum solution for this kind of problems by means of traditional approaches. Therefore, a hybrid multi-objective algorithm based on shuffled frog-leaping algorithm (SFLA) and bacteria optimization (BO) are deployed. The performance of the proposed hybrid algorithm is then compared with three well-known genetic algorithms, i.e. PS-NC GA, NSGA-II, and SPEA-II. The computational results show that the proposed hybrid algorithm outperforms the existing genetic algorithms, significantly in large-sized problems.     

Header Detection to Improve Multimedia Quality Over Wireless Networks

Wireless multimedia studies have revealed that forward error correction (FEC) on corrupted packets yields better bandwidth utilization and lower delay than retransmissions. To facilitate FEC-based recovery, corrupted packets should not be dropped so that maximum number of packets is relayed to a wireless receiver's FEC decoder. Previous studies proposed to mitigate wireless packet drops by a partial checksum that ignored payload errors. Such schemes require modifications to both transmitters and receivers, and incur packet-losses due to header errors. In this paper, we introduce a receiver-based scheme which uses the history of active multimedia sessions to detect transmitted values of corrupted packet headers, thereby improving wireless multimedia throughput. Header detection is posed as the decision-theoretic problem of multihypothesis detection of known parameters in noise. Performance of the proposed scheme is evaluated using trace-driven video simulations on an 802.11b local area network. We show that header detection with application layer FEC provides significant throughput and video quality improvements over the conventional UDP/IP/802.11 protocol stack     

Modified transpose Jacobian control of robotic systems

The simplicity of Transpose Jacobian (TJ) control is a significant characteristic of this algorithm for controlling robotic manipulators. Nevertheless, a poor performance may result in tracking of fast trajectories, since it is not dynamics-based. Use of high gains can deteriorate performance seriously in the presence of feedback measurement noise. Another drawback is that there is no prescribed method of selecting its control gains. In this paper, based on feedback linearization approach a Modified TJ (MTJ) algorithm is presented which employs stored data of the control command in the previous time step, as a learning tool to yield improved performance. The gains of this new algorithm can be selected systematically, and do not need to be large, hence the noise rejection characteristics of the algorithm are improved. Based on Lyapunov's theorems, it is shown that both the standard and the MTJ algorithms are asymptotically stable. Analysis of the required computational effort reveals the efficiency of the proposed MTJ law compared to the Model-based algorithms. Simulation results are presented which compare tracking performance of the MTJ algorithm to that of the TJ and Model-Based algorithms in various tasks. Results of these simulations show that performance of the new MTJ algorithm is comparable to that of Computed Torque algorithms, without requiring a priori knowledge of plant dynamics, and with reduced computational burden. Therefore, the proposed algorithm is well suited to most industrial applications where simple efficient algorithms are more appropriate than complicated theoretical ones with massive computational burden.     

Radiation induced decontamination of Cr(Ⅵ), Cu(Ⅱ) and phenol in some tannery effluents

Industrialization has led to a number of environmental problems, such as release of toxic metals and other toxic organic and inorganic compounds to the environment. Among all, the rapid expansion of leather related industries in Pakistan have resulted in considerable environmental problems and effluents from processing of both domestic and imported hides and skins have increased pollution to alarming levels. Some tannery effluents of Peshawar area investigated in the present study showed high concentrations of Cr(Ⅵ) (2.7-12.6 mg/L), Cu(Ⅱ) (2.6-11.4 mg/L) and phenol (0.1-4.2 mg/L). These contaminants are very toxic and must be removed from effluents before releasing into water bodies. A new technique of gamma irradiation has been investigated to decrease the load of COD and concentrations of Cr(Ⅵ), Cu(Ⅱ) and phenol associated with tannery effluents to the permitted values. It was observed that concentration of Cr(Ⅵ) in the effluents can be brought to the permitted level by applying radiation dose of 3 kGy. A radiation dose of 2.5 kGy was required to remove more than 95% of Cu(Ⅱ) and 100 % degradation of phenol in tannery effluents could be achieved by only one kGy of radiation dose.     

Critical heat flux prediction by using radial basis function and multilayer perceptron neural networks: A comparison study

Critical heat flux (CHF) is an important parameter for the design of nuclear reactors. Although many experimental and theoretical researches have been performed, there is not a single correlation to predict CHF because it is influenced by many parameters. These parameters are based on fixed inlet, local and fixed outlet conditions. Artificial neural networks (ANNs) have been applied to a wide variety of different areas such as prediction, approximation, modeling and classification. In this study, two types of neural networks, radial basis function (RBF) and multilayer perceptron (MLP), are trained with the experimental CHF data and their performances are compared. RBF predicts CHF with root mean square (RMS) errors of 0.24%, 7.9%, 0.16% and MLP predicts CHF with RMS errors of 1.29%, 8.31% and 2.71%, in fixed inlet conditions, local conditions and fixed outlet conditions, respectively. The results show that neural networks with RBF structure have superior performance in CHF data prediction over MLP neural networks. The parametric trends of CHF obtained by the trained ANNs are also evaluated and results reported.     

A method to eliminate the effect of radiation on thermocouple performance

In-core temperature measurements are pivotal in maintaining nuclear reactors in a safe state of operation. Thermocouples serve as the liaison in ensuring this safe state. The realization of the thermocouple's full potential is hindered by the fact that thermocouples cannot be situated in areas with high radiation fields. Radiation has the potential of generating voltages in the thermocouple wires, hence producing an error in the temperature transmitter output. In this paper, a mathematical model is developed to quantify the effect that radiation from the Canada Deuterium Uranium (CANDU) Nuclear Power Plants (NPPs) has on the thermocouple temperature reading. Subsequently, a method to offset the effect of radiation on the thermocouple is proposed. Simulation is performed to verify the effectiveness of the proposed system.     

Modeling the Effect of Subjective Factors on Productivity of Trenchless Technology Application to Buried Infrastructure Systems

Trenchless technology (TT) includes a large family of methods utilized for installing and rehabilitating underground utility systems with minimal surface disruption and destruction resulting from conventional excavation. Productivity of TT techniques is affected by a number of subjective factors that need to be evaluated. A productivity index (PI) model is developed in order to represent this subjective effect in refining productivity assessment. The analytic hierarchy process and fuzzy logic are used to develop the proposed PI model that relies on the actual performance of 12 subfactors under three main categories: management, environmental, and physical conditions. The developed PI model resulted in PI equal to 0.7323 and 0.7251 for microtunneling and horizontal directional drilling (HDD) projects, respectively. Multiattribute decision support system software is developed to determine the PI for a specific TT technique using Visual Basic. The PI model is tested, which shows reasonable results. This research is relevant to both industry practitioners and researchers. It provides practitioners with a model that justifies their productivity calculation by quantifying subjective factors effect, which will affect their schedule and cost estimation for trenchless projects. In addition, it provides researchers with the development methodology for the PI model.     

An analysis of peristaltic transport for flow of a Jeffrey fluid

Summary The peristaltic mechanism of a Jeffrey fluid in a circular tube is investigated. The rheological effects and compressibility of the fluid are taken into account. The modeled equations are solved using perturbation technique when the ratio of the wave amplitude to the radius of the pore is small. In the second order approximation, a net flow due to a travelling wave is obtained and effects of Reynolds number, relaxation and retardation times, compressibility of the fluid and tube radius are studied. It is noticed that for the Jeffrey fluid the back flow only occurs for large values of the relaxation time and small values of the retardation time (less than 10 in the present analysis). Another interesting observation is that oscillatory behavior of the net flow rate in the Jeffrey fluid is less than that of a Maxwell fluid. Several results of other fluid models can be deduced as the limiting cases of our situation.     

Effect of pressure on the structural and elastic properties of ZnS and MgS alloys in the B3 and B1 phases

From the first-principles calculations, we have investigated the elastic stiffness coefficients C₁₁, C₁₂, C₄₄ and the bulk modulus B of the II-VI semiconductors ZnS and MgS under hydrostatic pressure. The calculations are based on the density functional theory within the generalized gradient approximation (GGA) for exchange-correlation interaction. For the structural properties we have shown that ZnS adopt the rocksalt (NaCl or B1) structure over 11.87 GPa pressure, the same character is adopted by MgS over 0.8 GPa. The elastic coefficients have the same behavior for the different structures of alloys; they increase with increasing pressure values. Our results for the structural parameters and equilibrium phase elastic constants are in good agreement with the available theoretical and experimental data.     

Impedance studies on plasticized PMMA-LiX [X: CF3SO3, N(CF3SO2)2−] polymer electrolytes

Plasticized polymer electrolytes composed of poly(methylmethacrylate) (PMMA) as the host, propylene carbonate (PC) or ethylene carbonate (EC) as a plasticizer and LiX (X: CF₃SO₃⁻ or N(CF₃SO₂)₂⁻) as a salt were prepared by the solution cast technique. Impedance spectroscopy was performed in the temperature range between 303 and 383 K. In this paper, we report the electrical properties of polymer electrolytes with different lithium salts and plasticizers. The polymer electrolytes investigated exhibited high ionic conductivity at room temperature in the range of 10^− 6 to 10^− 4 S cm^− 1. The temperature dependence studies showed that the samples were ionic conductors and seemed to obey the Vogel-Tamman-Fulcher (VTF) rule. FTIR spectroscopy studies confirmed the polymer-salt interaction.