Sensing and identification of carbon monoxide using carbon films fabricated by methane arc discharge decomposition technique

Carbonaceous materials have recently received attention in electronic applications and measurement systems. In this work, we demonstrate the electrical behavior of carbon films fabricated by methane arc discharge decomposition technique. The current-voltage (I-V) characteristics of carbon films are investigated in the presence and absence of gas. The experiment reveals that the current passing through the carbon films increases when the concentration of CO₂ gas is increased from 200 to 800 ppm. This phenomenon which is a result of conductance changes can be employed in sensing applications such as gas sensors.     

Laboratory Investigation of Stilling Basin Slope Effect on Bed Scour at Downstream of Stepped Spillway: Physical Modeling of Javeh RCC Dam

Stepped spillway and stilling basin are one of the most important energy dissipation structures. Eventhough, most of energy dissipated by these structures, but in skimming flow, the upstream flow motion is nonaerated and the residual energy capable to destroyed structures during floods. In this study, effect of stilling basin slope on bed scour, downstream of Javeh dam was investigating. Experiments performed in hydraulic structures laboratory of the University of Kerman with six different discharges (5, 7, 13, 17, 25 and 30 l/s.m) and five various stilling basin slope (0.02, 0.01, 0, ?0.01 and???0.02). The parameters such as maximum scour depth (d_s), flow velocity (in three point), water depth on upstream and downstream of stepped spillway and stilling basin, the distance of the maximum scour depth to sill (L_s) and the gheometery of scour hole measured. Result shown that when stilling basin slopes was 0.02, the average of maximum relative scour depth, 47% Increased and in ?0.02, 52.2% Decreased. In addition, the distance of maximum scour depth until stilling basin increased by increasing and decreased by decreasing the stilling basin slope.

     

An investigation on the sensitivity analysis of the parameters of proposed wax precipitation model

Reservoir hydrocarbon fluids contain heavy paraffins that may form solid phase of wax at low temperatures. Formation of solid phases is highly unwanted in oil production assemblies, pipelines and in process equipments. A predictive technique is crucial to the solution of wax formation to alleviate this problem.The effect of different parameters to predict the conditions under which wax precipitation takes place using the proposed model of Sahand University of Technology and other models has been investigated. The proposed model uses regular solution theory to describe solid phase (wax) non-ideality and the liquid and gas phases are being described by an equation of state.In order to evaluate the reliability of the proposed model, wax appearance temperatures (WAT's) were calculated for several mixtures at different compositions and compared with different models. The proposed model predictions had very good agreement with experimental data over a wide range of compositional distributions in comparison with other models. Solid wax content was also calculated at different temperatures below WAT in several synthetic systems made up of a solvent (decane) and a paraffinic heavy fraction. The results of calculating the amount of wax precipitation showed very good agreement with experimental data. Effect of different parameters including fusion temperature (T^f), Enthalpy of fusion (Δh^f), solubility parameter (δ^S), and binary interaction parameters (BIP) in predicting the WAT and the amount of wax precipitated for different oil mixtures have been evaluated using the proposed model and compared with other models. The results showed that the T^f is the most sensitive parameter while δ^S shows the least sensitivity in matching the WAT. Even though using Δh^f could provide the same results as tuning T^f, but the required changes are much higher and sometimes not practical. Also using BIP as the tuning parameter, requires a fairly large coefficient that makes it unsuitable to be considered as the tuning parameter.     

Solving non-convex economic dispatch problem with valve point effects using modified group search optimizer method

This paper presents a novel solution based on the group search optimizer (GSO) methodology in order to determine the feasible optimal solution of the economic dispatch (ED) problem considering valve loading effects. The basic disadvantage of the original GSO algorithm is the fact that it gives a near-optimal solution rather than an optimal one in a limited runtime period. In this paper, a new modified group search optimizer (MGSO) is presented for improving the scrounger and ranger operators of GSO. The proposed MGSO is applied on different test systems and compared with most of the recent methodologies. The results show the effectiveness of the proposed method and prove that MGSO can be applicable for solving the power system economic load dispatch problem, especially in large scale power systems.     

A fast and noise resilient cluster-based anomaly detection

Clustering, while systematically applied in anomaly detection, has a direct impact on the accuracy of the detection methods. Existing cluster-based anomaly detection methods are mainly based on spherical shape clustering. In this paper, we focus on arbitrary shape clustering methods to increase the accuracy of the anomaly detection. However, since the main drawback of arbitrary shape clustering is its high memory complexity, we propose to summarize clusters first. For this, we design an algorithm, called Summarization based on Gaussian Mixture Model (SGMM), to summarize clusters and represent them as Gaussian Mixture Models (GMMs). After GMMs are constructed, incoming new samples are presented to the GMMs, and their membership values are calculated, based on which the new samples are labeled as “normal” or “anomaly.” Additionally, to address the issue of noise in the data, instead of labeling samples individually, they are clustered first, and then each cluster is labeled collectively. For this, we present a new approach, called Collective Probabilistic Anomaly Detection (CPAD), in which, the distance of the incoming new samples and the existing SGMMs is calculated, and then the new cluster is labeled the same as of the closest cluster. To measure the distance of two GMM-based clusters, we propose a modified version of the Kullback–Libner measure. We run several experiments to evaluate the performances of the proposed SGMM and CPAD methods and compare them against some of the well-known algorithms including ABACUS, local outlier factor (LOF), and one-class support vector machine (SVM). The performance of SGMM is compared with ABACUS using Dunn and DB metrics, and the results indicate that the SGMM performs superior in terms of summarizing clusters. Moreover, the proposed CPAD method is compared with the LOF and one-class SVM considering the performance criteria of (a) false alarm rate, (b) detection rate, and (c) memory efficiency. The experimental results show that the CPAD method is noise resilient, memory efficient, and its accuracy is higher than the other methods.     

An adaptive neuro‐fuzzy system for stock portfolio analysis

We propose an adaptive neuro‐fuzzy inference system (ANFIS) for stock portfolio return prediction. Previous work has shown that portfolio optimization can be improved by using predicted stock earnings rather than historical earnings. We show that predicted portfolio returns can be improved by using ANFIS and taking as input a variety of technical and fundamental attributes about various indices of the stock market. To generate membership functions, we use a robust noise rejection‐clustering algorithm. The neuro‐fuzzy model is tested on portfolios constituted from the Tehran Stock Exchange. In our experiments, the proposed method performs better in predicting the portfolio return than the classical Markowitz portfolio optimization method, a multiple regression, a neural network, and the Sugeno–Yasukawa method. © 2010 Wiley Periodicals, Inc.     

Direct adaptive fuzzy control with membership function tuning

In this paper, a direct self‐structured adaptive fuzzy control is introduced for the class of nonlinear systems with unknown dynamic models. Control is accomplished by an adaptive fuzzy system with a fixed number of rules and adaptive membership functions. The reference signal and state errors are used to tune the membership functions and update them instantaneously. The Lyapunov synthesis method is also used to guarantee the stability of the closed loop system. The proposed control scheme is applied to an inverted pendulum and a magnetic levitation system, and its effectiveness is shown via simulation. Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Supervised principal component analysis: Visualization, classification and regression on subspaces and submanifolds

We propose “supervised principal component analysis (supervised PCA)”, a generalization of PCA that is uniquely effective for regression and classification problems with high-dimensional input data. It works by estimating a sequence of principal components that have maximal dependence on the response variable. The proposed supervised PCA is solvable in closed-form, and has a dual formulation that significantly reduces the computational complexity of problems in which the number of predictors greatly exceeds the number of observations (such as DNA microarray experiments). Furthermore, we show how the algorithm can be kernelized, which makes it applicable to non-linear dimensionality reduction tasks. Experimental results on various visualization, classification and regression problems show significant improvement over other supervised approaches both in accuracy and computational efficiency.     

Hybrid binary arithmetic optimization algorithm with simulated annealing for feature selection in high-dimensional biomedical data

Gene expression data play a significant role in the development of effective cancer diagnosis and prognosis techniques. However, many redundant, noisy, and irrelevant genes (features) are present in the data, which negatively affect the predictive accuracy of diagnosis and increase the computational burden. To overcome these challenges, a new hybrid filter/wrapper gene selection method, called mRMR-BAOAC-SA, is put forward in this article. The suggested method uses Minimum Redundancy Maximum Relevance (mRMR) as a first-stage filter to pick top-ranked genes. Then, Simulated Annealing (SA) and a crossover operator are introduced into Binary Arithmetic Optimization Algorithm (BAOA) to propose a novel hybrid wrapper feature selection method that aims to discover the smallest set of informative genes for classification purposes. BAOAC-SA is an enhanced version of the BAOA in which SA and crossover are used to help the algorithm in escaping local optima and enhancing its global search capabilities. The proposed method was evaluated on 10 well-known microarray datasets, and its results were compared to other current state-of-the-art gene selection methods. The experimental results show that the proposed approach has a better performance compared to the existing methods in terms of classification accuracy and the minimum number of selected genes.

     

FTR: features tree based routing in mobile social networks

Mobile social networks are among subsets of delay tolerant networks. The nodes of these networks are mobile and the communication between them is done wireless and all nodes have social characteristics. The connection between these nodes is temporary and there is not end to end route between the source and the destination. Therefore, it is difficult to deliver the packets to the destination. One of the best routing methods in such networks is to use the information about the network context. These methods require the process of information collection and replicate the packets based on the context to increase the delivery ratio and enforce great overhead onto the network. Since the nodes have social characteristics and these features exist within the network, it seems that using them within the routing can be useful. In this paper a community based method of delivering is proposed that uses social characteristics of the individual members for routing the packets. Using the predetermined roles for the members that do not need data collection level can improve routing in these networks. In this method a tree is formed and each group of the members is entitled in one branch of it. The transmission of the packets from the source to the destination is done based on the differences between their characteristics and through the route which has the highest number of the nodes. The simulation results show that the delivery ratio of this method has increased regarding the related works and the overhead ratio has decreased.