UV-shielding by a polyurethane/f-Oil fly ash-CeO2 protective coating

A waste material called oil fly ash (OFA) was acid-functionalized, yielding f-OFA-COOH, which was then reacted with cerium oxide (CeO₂) to make CeO₂-functionalized OFA, or f-OFA-CeO₂. Pristine OFA and f-OFA-CeO₂ were used to make waterborne polyurethane (WBPU) dispersions, referred to as WBPU/OFA and WBPU/f-OFA-CeO₂, respectively, with defined OFA and f-OFA-CeO₂ content. All the dispersions were applied to mild steel as organic coatings to evaluate their protective properties, such as their hydrophobicity, adhesive strength and UV-shielding resistance. These protective properties varied based on the OFA and f-OFA-CeO₂ content. The highest water contact angle, minimum water swelling and maximum adhesive strength were found using WBPU/f-OFA-CeO₂-20 coating (using 2.00 wt% f-OFA-CeO₂), which also showed the maximum ultraviolet (UV) absorption via UV–vis spectroscopy analysis. This UV shielding result also matched field test results, as that coating was found to exhibit the lowest UV degradation near a marine atmosphere, as shown by X-ray photoelectron spectroscopy (XPS) analysis. The least affected hydrophobicity was also recorded for the sample with the WBPU/f-OFA-CeO₂-20 coating.     

Investigation of Creep Feed Grinding Parameters and Heat treatment Effects on the Nickel-base Superalloys

The Nickel base Superalloys are the most famous complicated and useable of Superalloys to make hot zone components of the gas turbines. The complicated dimensional tolerances, specially at the root of the blade show importance of grinding processes at the production of blades root. The prediction of the effect of machining parameters on the soundness of component surface strengthening for reaching to a suitable surface finishing and avoiding from crack formation at the work part during machining operation often is not easy and feasible so needs to more industrial investigation.This research is about frame 5 blade designed by GE and made from Superalloy IN738LC has been investigated. The formation of a plastically deformed and heat affected zone during grinding of Superalloy IN738LC with a high depth of cut but slow work speed (creep feed grinding) was investigated. Parameters such as work speed, depth of cut and radial dressing speed have been considered as variables and their effects have been studied. During experimental performed, the voltage and current of motor measured and power and special energy calculated.Some samples heat-treated (of the 1176℃ for 1 hr under neutral argon gas and cooling rate of 15℃/min up to 537℃ and then air cooling) to study grains recrystallization. Other samples have been created from the roots of blades and then coated by Nickel to measure boundary layer micro-hardness. The results show that increasing work speed leads to increasing the use power. Increasing the depth of cut, by increasing material removal rate, and the radial dressing speed, by decreasing power, lead to decreasing special energy. The temperature created by grinding lead to decreasing plastic deformation and boundary layer formation. When the radial dressing speed changes from 1 to 0.6μm/rev and other parameters are kept unchanged the roughness of surface increases and the special energy decreases. Sufficient dressing is very essential in limiting the width of the molten zone to few micrometers. As a result, it was found that local melting at contact spots to be a rather common mechanism during grinding of superalloys, lead to so-called white layers which can easily be observed on metallographic cross sections.     

Modified particle swarm optimization and fuzzy regularization for pseudo de-convolution of spatially variant blurs

We propose a modified particle swarm optimization (MPSO) based method for Pseudo De-convolution of the ill-posed inverse problem namely, the space-variant image degradation (SVD). In this paper, SVD is simulated by the pseudo convolution of different sub-regions of the image with different known blurring kernels and additive random noise with unknown variance. Two heuristic modifications are proposed in PSO: 1) Initialization of the swarm and 2) Mutation of the global best. Fuzzy logic is applied for the computation of regularization parameter (RP) to cater for the sensitivity of the problem. The computation of RP is crucial due to the additive noise in the SVD image. Thus mathematical morphology (MM) is applied for better extraction of spatial activity from the distorted image. The performance of the proposed method is evaluated with different test images and noise powers. Comparative analysis demonstrates the superiority of proposed restoration, in terms of quantitative measures, over well-known existing and state-of-the-art SVD approaches.     

Majority bargaining for resource division

We address the problem of how a set of agents can decide to share a resource, represented as a unit-sized pie. The pie can be generated by the entire set but also by some of its subsets. We investigate a finite horizon non-cooperative bargaining game, in which the players take it in turns to make proposals on how the resource should for this purpose be allocated, and the other players vote on whether or not to accept the allocation. Voting is modelled as a Bayesian weighted voting game with uncertainty about the players’ weights. The agenda, (i.e., the order in which the players are called to make offers), is defined exogenously. We focus on impatient players with heterogeneous discount factors. In the case of a conflict, (i.e., no agreement by the deadline), no player receives anything. We provide a Bayesian subgame perfect equilibrium for the bargaining game and conduct an ex-ante analysis of the resulting outcome. We show that the equilibrium is unique, computable in polynomial time, results in an instant Pareto optimal outcome, and, under certain conditions provides a foundation for the core and also the nucleolus of the Bayesian voting game. In addition, our analysis leads to insights on how an individual’s bargained share is influenced by his position on the agenda. Finally, we show that, if the conflict point of the bargaining game changes, then the problem of determining the non-cooperative equilibrium becomes NP-hard even under the perfect information assumption. Our research also reveals how this change in conflict point impacts on the above mentioned results.     

Voiced/non-voiced speech classification using adaptive thresholding with bivariate EMD

This paper introduces a robust voiced/non-voiced (VnV) speech classification method using bivariate empirical mode decomposition (bEMD). Fractional Gaussian noise (fGn) is employed as the reference signal to derive a data adaptive threshold for VnV discrimination. The analyzing speech signal and fGn are combined to generate a complex signal which is decomposed into a finite number of complex-valued intrinsic mode functions (IMFs) by using bEMD. The real and imaginary parts of the IMFs represent the IMFs of observed speech and fGn, respectively. The log-energies of both types of IMFs are calculated. There exist similarities between the IMF log-energy representation of fGn and unvoiced speech signals. Hence, the upper confidence limit from IMF log-energies of fGn is used as data adaptive threshold for VnV classification. If the subband log-energy of speech segment exceeds the threshold, the segment is classified as voiced and unvoiced otherwise. The experimental results show that the proposed algorithm performs better than the recently reported methods without requiring any training data for a wide range of SNRs.     

A Comparison of Mamdani and Sugeno Fuzzy Based Packet Scheduler for MANET with a Realistic Wireless Propagation Model

 The mobile nature of the nodes in a wireless mobile ad-hoc network (MANET) and the error prone link connectivity between nodes pose many challenges. These include frequent route changes, high packet loss, etc. Such problems increase the end-toend delay and decrease the throughput. This paper proposes two adaptive priority packet scheduling algorithms for MANET based on Mamdani and Sugeno fuzzy inference system. The fuzzy systems consist of three input variables: data rate, signal-to-noise ratio (SNR) and queue size. The fuzzy decision system has been optimised to improve its efficiency. Both fuzzy systems were verified using the Matlab fuzzy toolbox and the performance of both algorithms were evaluated using the riverbed modeler (formally known as OPNET modeler). The results were compared to an existing fuzzy scheduler under various network loads, for constant-bit-rate (CBR) and variable-bit-rate (VBR) traffic. The measuring metrics which form the basis for performance evaluation are end-to-end delay, throughput and packet delivery ratio. The proposed Mamdani and Sugeno scheduler perform better than the existing scheduler for CBR traffic. The end-to-end delay for Mamdani and Sugeno scheduler was reduced by an average of 52% and 54%, respectively. The performance of the throughput and packet delivery ratio for CBR traffic are very similar to the existing scheduler because of the characteristic of the traffic. The network was also at full capacity. The proposed schedulers also showed a better performance for VBR traffic. The end-to-end delay was reduced by an average of 38% and 52%, respectively. Both the throughput and packet delivery ratio (PDR) increased by an average of 53% and 47%, respectively. The Mamdani scheduler is more computationally complex than the Sugeno scheduler, even though they both showed similar network performance. Thus, the Sugeno scheduler is more suitable for real-time applications.     

Development and Verification of Simulation Model Based on Real MANET Experiments for Transport Layer Protocols （UDP and TCP）

There is a lack of appropriate guidelines for realistic user traces, mobility models, routing protocols, considerations of real-life challenges, etc. for general-purpose mobile ad hoc networks (MANET). In this paper, four laptops are used in an open field environment in four scenarios to evaluate the performances of Internet control message protocol (ICMP) based ping and transmission control protocol (TCP) based streaming video applications using optimised link state routing (OLSR) implementation in an IEEE 802.11g wireless network. Corresponding simulations are developed in Network Simulator ns-2 by setting simulation parameters according to the real experiments. Difficulties faced to regenerate real-life scenarios have been discussed and the gaps between reality and simulation are identified. A setup guideline to produce realistic simulation results has been established.     

A component‐based framework for radio‐astronomical imaging software systems

This paper describes a component‐based framework for radio‐astronomical imaging software systems. We consider optimal re‐use strategies for packages of disparate architectures brought together within a modern component framework. In this practical case study, the legacy codes include both procedural and object‐oriented architectures. We consider also the special requirements on scientific component middleware, with a specific focus on high‐performance computing. We present an example application in this component architecture and outline future development planned for this project. Copyright © 2007 John Wiley & Sons, Ltd.     

Data Compression for Proof Replay

We describe a compressing translation from SAT solver generated propositional resolution refutation proofs to classical natural deduction proofs. The resulting proof can usually be checked quicker than one that simply simulates the original resolution proof. We use this result in interactive theorem provers, to speed up reconstruction of SAT solver generated proofs. The translation is fast and scales up to large proofs with millions of inferences.     

Unsupervised and simultaneous training of multiple object detectors from unlabeled surveillance video

Object detection is an essential component in automated vision-based surveillance systems. In general, object detectors are constructed using training examples obtained from large annotated data sets. The inevitable limitations of typical training data sets make such supervised methods unsuitable for building generic surveillance systems applicable to a wide variety of scenes and camera setups. In our previous work we proposed an unsupervised method for learning and detecting the dominant object class in a general dynamic scene observed by a static camera. In this paper, we investigate the possibilities to expand the applicability of this method to the problem of multiple dominant object classes. We propose an idea on how to approach this expansion, and perform an evaluation of this idea using two representative surveillance video sequences.