水声网络全双工定向碰撞避免媒体接入控制协议

近年来水声网络(UAN)技术飞速发展,但仍然面临诸多严峻挑战,能量效率成为水声网络的首要考虑因素。此外,水声信道传播时延大且可用带宽受限,严重制约了水声通信技术的可靠性和有效性,进而限制了水声网络的整体性能。定向通信技术可以有效改善上述情况,通过波束聚焦能力将声波能量聚集在一定角度范围内,获得比全向通信更高的通信范围和信噪比,提升整个网络的能耗效率和空间复用率。但该技术需要对目的节点的位置具备先验知识,且会面临“聋节点”问题,因此该文提出一个水声网络全双工定向碰撞避免(FDDCA)媒体接入控制(MAC)协议,通过装备两个分别处于不同工作频带的全向换能器和定向换能器解决“聋节点”问题,并通过降低节点的冲突域解决了“暴露终端”问题。仿真结果表明,与水下Aloha(UW-Aloha)和时隙地面多址接入(S-FAMA)协议相比,FDDCA在多汇聚节点的网络拓扑下吞吐量分别提升了140%和400%,网络能效上节省了90%和94%。     

Optimization of a proton exchange membrane fuel cell membrane electrode assembly

A computational framework for fuel cell analysis and optimization is presented as an innovative alternative to the time consuming trial-and-error process currently used for fuel cell design. The framework is based on a two-dimensional through-the-channel isothermal, isobaric and single phase membrane electrode assembly (MEA) model. The model input parameters are the manufacturing parameters used to build the MEA: platinum loading, platinum to carbon ratio, electrolyte content and gas diffusion layer porosity. The governing equations of the fuel cell model are solved using Netwon’s algorithm and an adaptive finite element method in order to achieve near quadratic convergence and a mesh independent solution respectively. The analysis module is used to solve the optimization problem of finding the optimal MEA composition for maximizing performance. To solve the optimization problem a gradient-based optimization algorithm is used in conjunction with analytical sensitivities. By using a gradient-based method and analytical sensitivities, the framework presented is capable of solving a complete MEA optimization problem with state-of-the-art electrode models in approximately 30 min, making it a viable alternative for solving large-scale fuel cell problems.     

Learning from demonstration in robots: Experimental comparison of neural architectures

Robots have played an important role in the automation of computer aided manufacturing. The classical robot control implementation involves an expensive key step of model-based programming. An intuitive way to reduce this expensive exercise is to replace programming with machine learning of robot actions from demonstration where a (learner) robot learns an action by observing a demonstrator robot performing the same. To achieve this learning from demonstration (LFD) different machine learning techniques such as Artificial Neural Networks (ANN), Genetic Algorithms, Hidden Markov Models, Support Vector Machines, etc. can be used. This piece of work focuses exclusively on ANNs. Since ANNs have many standard architectural variations divided into two basic computational categories namely the recurrent networks and feed-forward networks, representative networks from each have been selected for study, i.e. Feed Forward Multilayer Perceptron (FF) network for feed-forward networks category and Elman (EL), and Nonlinear Autoregressive Exogenous Model (NARX) networks for the recurrent networks category. The main objective of this work is to identify the most suitable neural architecture for application of LFD in learning different robot actions. The sensor and actuator streams of demonstrated action are used as training data for ANN learning. Consequently, the learning capability is measured by comparing the error between demonstrator and corresponding learner streams. To achieve fairness in comparison three steps have been taken. First, Dynamic Time Warping is used to measure the error between demonstrator and learner streams, which gives resilience against translation in time. Second, comparison statistics are drawn between the best, instead of weight-equal, configurations of competing architectures so that learning capability of any architecture is not forced handicap. Third, each configuration's error is calculated as the average of ten trials of all possible learning sequences with random weight initialization so that the error value is independent of a particular sequence of learning or a particular set of initial weights. Six experiments are conducted to get a performance pattern of each architecture. In each experiment, a total of nine different robot actions were tested. Error statistics thus obtained have shown that NARX architecture is most suitable for this learning problem whereas Elman architecture has shown the worst suitability. Interestingly the computationally lesser MLP gives much lower and slightly higher error statistics compared to the computationally superior Elman and NARX neural architectures, respectively.     

Fast attack detection using correlation and summarizing of security alerts in grid computing networks

Due to the extensive growth of grid computing networks, security is becoming a challenge. Usual solutions are not enough to prevent sophisticated attacks fabricated by multiple users especially when the number of nodes connected to the network is changing over the time. Attackers can use multiple nodes to launch DDoS attacks which generate a large amount of security alerts. On the one hand, this large number of security alerts degrades the overall performance of the network and creates instability in the operation of the security management solutions. On the other hand, they can help in camouflaging other real attacks. To address these issues, a?correlation mechanism is proposed which reduces the security alerts and continue detecting attacks in grid computing networks. To obtain the more accurate results, a?major portion of the experiments are performed by launching DDoS and Brute Force (BF) attacks in real grid environment, i.e., the Grid??5000 (G5K) network.     

A robust weakly compressible SPH method and its comparison with an incompressible SPH

This paper presents a comparative study for the weakly compressible (WCSPH) and incompressible (ISPH) smoothed particle hydrodynamics methods by providing numerical solutions for fluid flows over an airfoil and a square obstacle. Improved WCSPH and ISPH techniques are used to solve these two bluff body flow problems. It is shown that both approaches can handle complex geometries using the multiple boundary tangents (MBT) method, and eliminate particle clustering‐induced instabilities with the implementation of a particle fracture repair procedure as well as the corrected SPH discretization scheme. WCSPH and ISPH simulation results are compared and validated with those of a finite element method (FEM). The quantitative comparisons of WCSPH, ISPH and FEM results in terms of Strouhal number for the square obstacle test case, and the pressure envelope, surface traction forces, and velocity gradients on the airfoil boundaries as well as the lift and drag values for the airfoil geometry indicate that the WCSPH method with the suggested implementation produces numerical results as accurate and reliable as those of the ISPH and FEM methods. Copyright © 2011 John Wiley & Sons, Ltd.     

Sky conditions at Ibadan during 1975–1980

The sky conditions at Ibadan, characterized by the clearness index, relative sunshine, diffuse ratio and diffuse coefficient, are presented for the six years 1975 to 1980. It is shown that based on the monthly average of the daily clearness index , the annual sky conditions have six patterns, namely, the harmattan-haze dry season pattern (November, December, and January) with , the dustfree dry season pattern (February, March, and April) with , and four rainy season patterns of August ( ), July and September ( ), June and October ( ) and May ( ). The variations of the diffuse ratio and relative sunshine are also in tune with these six patterns while the diffuse coefficient has an almost constant value of about 0.24 for all seasons. Following the work of Liu and Jordan, statistical “monthly K_τ curves” are established for Ibadan along the lines of the six patterns of sky conditions. These monthly K_τ curves can be utilized to determine the approximate statistical distribution of the daily total radiation for other cities which are in the same weather zone as Ibadan when the monthly average daily total radiation is known for such cities.     

Κ‐CN gene polymorphism in Nili‐ravi buffalo,Achai and Sahiwal cattle of Pakistan

Kappa‐casein (κ‐CN) is the subtype of casein protein, an important constituent of bovine milk protein. The current study was undertaken to investigate the genetic polymorphism in κ‐CN gene of Nili‐ravi buffalo, Achai and Sahiwal cattle of Pakistan using polymerase chain reaction–restriction fragment length polymorphism (PCR‐RFLP) technique. The Nili‐ravi buffalo was found to be monomorphic (genotype BB only) for κ‐CN gene. Achai cattle were polymorphic for κ‐CN (having three genotypes AA, AB and BB) with a frequency of 0.70, 0.18 and 0.12, respectively, while in Sahiwal cattle, both the genotypes AA and AB were found with genotypic frequencies of 0.92 and 0.08, respectively. The presence of genotype BB in Achai cattle is surprising as it is absent in most of the cattle breeds worldwide.     

Design considerations for an automotive magnetorheological brake

In this paper, design considerations for building an automotive magnetorheological (MR) brake are discussed. The proposed brake consists of multiple rotating disks immersed in a MR fluid and an enclosed electromagnet. When current is applied to the electromagnet, the MR fluid solidifies as its yield stress varies as a function of the magnetic field applied. This controllable yield stress produces shear friction on the rotating disks, generating the braking torque. In this work, practical design criteria such as material selection, sealing, working surface area, viscous torque generation, applied current density, and MR fluid selection are considered to select a basic automotive MR brake configuration. Then, a finite element analysis is performed to analyze the resulting magnetic circuit and heat distribution within the MR brake configuration. This is followed by a multidisciplinary design optimization (MDO) procedure to obtain optimal design parameters that can generate the maximum braking torque in the brake. A prototype MR brake is then built and tested and the experimental results show a good correlation with the finite element simulation predictions. However, the braking torque generated is still far less than that of a conventional hydraulic brake, which indicates that a radical change in the basic brake configuration is required to build a feasible automotive MR brake.     

A relaxation trend in the electron spin resonance phenomena in copper tellurite glasses containing NiO,CoO and Lu2O3

Copper tellurite glasses containing NiO, CoO and Lu₂O₃ were prepared by the melt-quenching technique. The composition used was 65TeO₂-(35-x)CuO-xTMO (mol%), where TMO indicates NiO, CoO, Lu₂O₃, and for NiO- and CoO-doped glasses,x has the values 0, 0.5, 1 to 4, and for Lu₂O₃ doped glasses x=0 to 4. Electron spin resonance (ESR) spectra of all glasses were recorded at room temperature. The results on glasses doped with NiO, CoO and Lu₂O₃ are discussed in terms of oxidation-reduction, cross-relaxation and interelectronic repulsion processes, respectively. Cobalt oxide is found to be more effective in relaxing the ESR spectrum than nickel and lutetium oxides when substituted in copper tellurite glasses.