Impact of adhesive ingredients on adhesion between rubber and brass-plated steel wire in tire

Proficiency on underlying mechanism of rubber-metal adhesion has been increased significantly in the last few decades. Researchers have investigated the effect of various ingredients, such as hexamethoxymethyl melamine, resorcinol, cobalt stearate, and silica, on rubber-metal interface. The role of each ingredient on rubber-metal interfacial adhesion is still a subject of scrutiny. In this article, a typical belt skim compound of truck radial tire is selected and the effect of each adhesive ingredient on adhesion strength is explored. Out of these ingredients, the effect of cobalt stearate is found noteworthy. It has improved adhesion strength by 12% (without aging) and by 11% (humid-aged), respectively, over control compound. For detailed understanding of the effect of cobalt stearate on adhesion, scanning electron microscopy and energy dispersive spectroscopy are utilized to ascertain the rubber coverage and distribution of elements. X-ray photoelectron spectroscopy results helped us to understand the impact of Cu_XS layer depth on rubber-metal adhesion. The depth profile of the Cu_XS layer was found to be one of the dominant factors of rubber-metal adhesion retention. Thus, this study has made an attempt to find the impact of different adhesive ingredients on the formation of Cu_XS layer depth at rubber-metal interface and establish a correlation with adhesion strength simultaneously.     

Modeling Power Supply Noise in Delay Testing

Excessive power supply noise during test can cause overkill. This article discusses two models for supply noise in delay testing and their application to test compaction. The proposed noise models avoid complicated power network analysis, making them much faster than existing power noise analysis tools. can cause performance degradation and     

Optimisation of solar photovoltaic (PV) parameters using meta-heuristics

Microsystem Technologies - This paper presents a critical analysis of the meta-heuristic techniques used in various researches on the optimisation of photovoltaic (PV) parameters, which involves...     

Direction prediction for avoiding occlusion in visual surveillance

Occurrence of occlusion while providing visual surveillance leads to anarchy as the track of the subject under motion may be lost. This often results into the failure of the surveillance system. The approach of predicting motion of moving subjects and hence the chances of their mutual occlusion gives an upper hand to surveillance system to take in-time necessary action towards mitigation of loss of track during dynamic occlusion. Direction of motion of a moving subject plays a major role while studying its motion. Direction along with the velocity of a subject in a 3D plane completely describes the motion of any subject. This article proposes a model‘-based approach for direction prediction of a moving subject in a 3D global plane as acquired in a 2D camera plane. The proposed approach uses the eight discrete directions of motion as proposed in and models different directions. The proposed direction prediction method is experimentally verified with six different classifiers, i.e. regression analysis, simple logistic regression, MLP, k-NN, SVM and Bays classifier over existing as well as self-acquired databases. The initial simulation results are motivating as the overall accuracies achieved through different classifiers are of the range of 87–94 \(\%\), which advocates the suitability of the said approach.     

Design,simulation and analysis of RF MEMS capacitive shunt switches with high isolation and low pull-in-voltage

Microsystem Technologies - This paper presents the design a capacitive shunt type RF-MEMS switch with high isolation, high switching speed and low actuation voltage for Ka-band applications. The...     

IIR system identification using cat swarm optimization

Conventional derivative based learning rule poses stability problem when used in adaptive identification of infinite impulse response (IIR) systems. In addition the performance of these methods substantially deteriorates when reduced order adaptive models are used for such identification. In this paper the IIR system identification task is formulated as an optimization problem and a recently introduced cat swarm optimization (CSO) is used to develop a new population based learning rule for the model. Both actual and reduced order identification of few benchmarked IIR plants is carried out through simulation study. The results demonstrate superior identification performance of the new method compared to that achieved by genetic algorithm (GA) and particle swarm optimization (PSO) based identification.     

Flow separation in a viscous parabolic shear past a sphere

Summary Axisymmetric parabolic shear flow past a spinning sphere in an unbounded viscous medium is considered using the matching technique and taking the non-uniform shear as the dominant feature, where the Reynolds number based on parabolic shearR _e 1 and the rotational Reynolds number based on the angular rotation of the sphereR ₀1 are such thatR ₀ ²/R _e =O(1). Forces on the body are calculated and flow separation from the boundary is studied. Coalescion of two wakes or detachment of a wake into two are observed, depending on the increase in magnitudeU of the free stream for a fixedR ₀ ²/R _e or increase inR ₀ ²/R _e for a fixedU respectively.With 8 Figures     

The Effect of Distribution on Product Temperature Profile in Thermally Insulated Containers for Express Shipments

An uninterrupted cold chain is a continual series of storage and distribution activities that maintain a specific temperature or temperature range. Cold chain solutions typically involve excessive packaging to ensure that the desired product temperature is maintained through the distribution process, thereby increasing the logistics‐related costs. There is a myriad of solutions available for shipping temperature‐sensitive products, including those constructed with a variety of packaging materials as well as refrigerants. Although static characteristics for thermally insulated packaging solutions such as the R‐values of package systems as well as the melting points and heat absorption rates of various refrigerants have been studied in the past, none of the past studies have evaluated the effect of comprehensive distribution on the reliability of the cold chain packaging solutions. This research was undertaken to study the temperature profiles for factors such as different densities for a given thickness of thermally insulating material, wall thicknesses and distribution environments for four different types of materials—polyurethane, virgin expanded polystyrene, recycled content expanded polystyrene and vacuum‐insulated panels. The temperature range of 2 °C–8 °C, critical for pharmaceutical drugs and vaccines, was targeted. An interesting regression‐based finding was that the interaction between the R‐value and the wall thickness significantly influenced the length of time the thermally insulated packages stayed in the desired range of 2 °C–8 °C . The findings of this study will be decisive in designing cost‐efficient and practical single‐use cold chain transportation solutions for temperature‐sensitive products. Copyright © 2012 John Wiley & Sons, Ltd.     

Distributed and robust parameter estimation of IIR systems using incremental particle swarm optimization

In recent years because of substantial use of wireless sensor network the distributed estimation has attracted the attention of many researchers. Two popular learning algorithms: incremental least mean square (ILMS) and diffusion least mean square (DLMS) have been reported for distributed estimation using the data collected from sensor nodes. But these algorithms, being derivative based, have a tendency of providing local minima solution particularly for minimization of multimodal cost function. Hence for problems like distributed parameters estimation of IIR systems, alternative distributed algorithms are required to be developed. Keeping this in view the present paper proposes two population based incremental particle swarm optimization (IPSO) algorithms for estimation of parameters of noisy IIR systems. But the proposed IPSO algorithms provide poor performance when the measured data is contaminated with outliers in the training samples. To alleviate this problem the paper has proposed a robust distributed algorithm (RDIPSO) for IIR system identification task. The simulation results of benchmark IIR systems demonstrate that the proposed algorithms provide excellent identification performance in all cases even when the training samples are contaminated with outliers.