Effect of Extraordinary Large Floods on at-site Flood Frequency

The peak flow of extraordinary large floods that occur during a period of systematic record is a controversial problem for flood frequency analysis (FFA) using traditional methods. The present study suggests that such floods be treated as historic flood data even though their historical period is unknown. In this paper, the extraordinary large flood peak was first identified using statistical outlier tests and normal probability plots. FFA was then applied with and without the extraordinary large floods. In this step, two goodness-of-fit tests including mean absolute relative deviation and mean squared relative deviation were used to identify the best-fit probability distributions. Next, the generalized extreme value (GEV), three-parameter lognormal (LN3), log-Pearson type III (LP3), and Wakeby (WAK) probability distributions were used to incorporate and adjust the extraordinary large floods with other systematic data. Finally, procedures with and without historical adjustment were compared for the extraordinary large floods in terms of goodness-of-fit and flood return-period quantiles. The results of this comparison indicate that historical adjustment from an operational perspective was more viable than without adjustment procedure. Furthermore, the results without adjustment were unreasonable (subject to over- and under-estimation) and produced physically unrealistic estimates that were not compatible with the study area. The proposed approach substantially improved the probability estimation of rare floods for efficient design of hydraulic structures, risk analysis, and floodplain management.     

Hybrid Probabilistic Risk Assessment Using Fuzzy FTA and Fuzzy AHP in a Process Industry

Journal of Failure Analysis and Prevention - There are many available techniques which are widely used for failure probability analysis. Fault tree analysis (FTA) is a well-known method to identify...     

Biosynthesis of SeNPs by Mycobacterium bovis and their enhancing effect on the immune response against HBs antigens: an in vivo study

Conventional hepatitis B vaccine contains alum but is less effective to induce Th1 response. Selenium nanoparticles and Bacillus Calmette–Guerin were reported as immune modulators. In this study, SeNPs were extracted from Mycobacterium bovis and characterised. SeNPs were mixed with HBs‐Ag and administered to the mice to investigate he immune response pattern. With an addition of Se ions at a sub‐inhibitory concentration to the Sauton medium broth after 24 h, SeNPs were extracted from M. bovis and characterised by Fourier transform infrared spectroscopy, dynamic light scattering, atomic forcemicroscopy, energy dispersive X‐ray spectrum, transmission electron microscopy, and thermogravimetric analysis. Furthermore, female inbred BALB/c mice were injected subcutaneously on the first, 14th, 28th day with 100 and 200 µg doses of that SeNPs supplemented with HBs‐Ag vaccine. Later, the total antibody, isotypes of Immunoglobulin G1, Interlukin 4, and interferon‐γ were measured by enzye‐linked immunosorbent assay. The size of the SeNPs was <150 nm. Level of total antibody and immunoglobulin G2a increased significantly in the group that received 200 µg/ml nano selenium extracted from M. bovis. SeNPs in dose of 200 µg coated with organic materials of M. bovis could induce an influential immune response in relation to the conventional HBs‐Ag vaccine.Inspec keywords: selenium, nanoparticles, nanofabrication, microorganisms, Fourier transform spectra, infrared spectra, light scattering, atomic force microscopy, X‐ray chemical analysis, transmission electron microscopy, thermal analysis, nanomedicineOther keywords: biosynthesis, selenium nanoparticles, Mycobacterium bovis, enhnced immune response, HBs antigens, Bacillus Calmette‐Guerin, immune modulators, biogenic SeNPs, immune response pattern, Sauton medium broth, Fourier transform infrared spectroscopy, dynamic light scattering, atomic force microscopy, energy dispersive X‐ray spectrum, transmission electron microscopy, thermogravimetric analysis, female inbred BALB/c mice, antibody, IgG1 isotype, IgG2a isotype, IL₄ isotype, interferon‐γ, enzyme‐linked immunosorbent assay, bacterium, time 24 h, time 14 day, time 28 day, Se     

Multiple robust H∞ controller design using the nonsmooth optimization method

This paper proposes a systematic technique to design multiple robust H_∞ controllers. The proposed technique achieves a desired robust performance objective, which is impossible to achieve with a single robust controller, by dividing the uncertainty set into several subsets and by designing a robust controller to each subset. To achieve this goal with a small number of divisions of the uncertainty set, an optimization problem is formulated. Since the cost function of this optimization problem is not a smooth function, a numerical nonsmooth optimization algorithm is proposed to solve this problem. This method avoids the use of Lyapunov variables, and therefore it leads to a moderate size optimization problem. A numerical example shows that the proposed multiple robust control method can improve the closed‐loop performance when a single robust controller cannot achieve satisfactory performance. Copyright © 2009 John Wiley & Sons, Ltd.     

Unsupervised adaptive neural-fuzzy inference system for solving differential equations

There has been a growing interest in combining both neural network and fuzzy system, and as a result, neuro-fuzzy computing techniques have been evolved. ANFIS (adaptive network-based fuzzy inference system) model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. In this paper, a novel structure of unsupervised ANFIS is presented to solve differential equations. The presented solution of differential equation consists of two parts; the first part satisfies the initial/boundary condition and has no adjustable parameter whereas the second part is an ANFIS which has no effect on initial/boundary conditions and its adjustable parameters are the weights of ANFIS. The algorithm is applied to solve differential equations and the results demonstrate its accuracy and convince us to use ANFIS in solving various differential equations.     

Relaxed constraints support vector machines for noisy data

Real-world data collected for computer-based applications are frequently impure. Differentiation of outliers and noisy data from normal ones is a major task in data mining applications. On the other hand, elimination of noisy and outlier data from training samples of a dataset may lead to over-fitting or information loss. A fuzzy support vector machine (FSVM) provides an effective means to deal with this problem. It reduces the effect of the noisy data and outliers by using a fuzzy membership functions. In this paper, a new formation for SVMs is introduced that considers importance degrees for training samples. The constraints of the SVM are converted to fuzzy inequalities. The proposed method, RSVM, shows better efficiency in the classification of data in different domains. Especially, using the proposed RSVM for multi-class classification of arrhythmia disease is presented at the end of this paper as a practical case study to show the effectiveness of the proposed system.     

A Simulation-Based Optimization Model for Flood Management on a Watershed Scale

Using structural and nonstructural measures for flood damage reduction is a long-standing problem in water resources planning and management. In the present study, an algorithm is presented for the optimal design of structural and nonstructural flood mitigation measures based on simulation-based optimization approach. For this purpose, the MIKE-11 simulation model, a one-dimensional hydrodynamic model, was used to calculate the potential damages of different flood scenarios under the various combinations of structural and nonstructural measures and this model was coupled with the NSGA-II multi-objective optimization model to provide the optimal Pareto solutions between two conflict objectives of minimizing the investment costs of flood mitigation measures and the potential damages of the floodplain. The proposed model was then applied to a small watershed in central part of Iran as a case study and the optimal trade-off solutions were calculated for different flood scenarios. Using these trade-offs, for each level of funding, decision makers can assign the optimal design of flood mitigation measures considering decision criteria.     

A general insight into the effect of neuron structure on classification

This paper gives a general insight into how the neuron structure in a multilayer perceptron (MLP) can affect the ability of neurons to deal with classification. Most of the common neuron structures are based on monotonic activation functions and linear input mappings. In comparison, the proposed neuron structure utilizes a nonmonotonic activation function and/or a nonlinear input mapping to increase the power of a neuron. An MLP of these high power neurons usually requires a less number of hidden nodes than conventional MLP for solving classification problems. The fewer number of neurons is equivalent to the smaller number of network weights that must be optimally determined by a learning algorithm. The performance of learning algorithm is usually improved by reducing the number of weights, i.e., the dimension of the search space. This usually helps the learning algorithm to escape local optimums, and also, the convergence speed of the algorithm is increased regardless of which algorithm is used for learning. Several 2-dimensional examples are provided manually to visualize how the number of neurons can be reduced by choosing an appropriate neuron structure. Moreover, to show the efficiency of the proposed scheme in solving real-world classification problems, the Iris data classification problem is solved using an MLP whose neurons are equipped by nonmonotonic activation functions, and the result is compared with two well-known monotonic activation functions.     

Noise analysis and characterization of a sigma-delta capacitive microaccelerometer

This paper reports a high-sensitivity low-noise capacitive accelerometer system with one micro-g//spl radic/Hz resolution. The accelerometer and interface electronics together operate as a second-order electromechanical sigma-delta modulator. A detailed noise analysis of electromechanical sigma-delta capacitive accelerometers with a final goal of achieving sub-/spl mu/g resolution is also presented. The analysis and test results have shown that amplifier thermal and sensor charging reference voltage noises are dominant in open-loop mode of operation. For closed-loop mode of operation, mass-residual motion is the dominant noise source at low sampling frequencies. By increasing the sampling frequency, both open-loop and closed-loop overall noise can be reduced significantly. The interface circuit has more than 120 dB dynamic range and can resolve better than 10 aF. The complete module operates from a single 5-V supply and has a measured sensitivity of 960 mV/g with a noise floor of 1.08 /spl mu/g//spl radic/Hz in open-loop. This system can resolve better than 10 /spl mu/g//spl radic/Hz in closed-loop.