On scaling the IEEE 802.11 to facilitate scalable wireless networks

The IEEE 802.11 MAC protocol has gained widespread popularity and has been adopted as the de-facto layer 2 protocol for wireless local area networks (WLANs). However, it is well known that as the number of competing stations increases, the performance of the protocol degrades dramatically. Given the explosive growth in WLANs’ usage, the question of how to sustain each user’s perceived performance when a large number of competing stations are present, is an important and challenging open research problem.Motivated by this, in this paper we analyze the behavior of 802.11-based WLANs as the number of competing stations increases, and attempt to provide concrete answers to the following fundamental questions: (i) is there a set of system and protocol parameters that we can scale in order to sustain each individual user’s perceived performance, and (ii) what is the minimum scaling factor?Using theoretical analysis coupled with extensive simulations we show that such a set of parameters exists, and that the minimum scaling factor is equal to the factor by which the number of users increases. Our results reveal several important scaling properties that exist in today’s 802.11-based wireless networks, and set guidelines for designing future versions of such networks that can efficiently support a very large number of users.     

New robust stability analysis for genetic regulatory networks with random discrete delays and distributed delays

This paper investigates the delay-probability-distribution-dependent stability problem of stochastic genetic regulatory networks (GRNs) with random discrete time delays and distributed time delays which exist in both translation process and feedback regulation process. The information of the probability distribution of the discrete time delays is considered and transformed into parameter matrices of the GRN models. By introducing a new Lyapunov functional which takes into account the ranges of delays and employing some free-weighting matrices, some new delay-probability-distribution-dependent stability criteria are established in the form of linear matrix inequalities (LMIs) to guarantee the GRNs to be asymptotically stable in the mean square. In addition, when estimating the upper bounds of the derivative of Lyapunov functionals, we carefully handle the additional useful terms about the distributed delays, which may lead to the less conservative results. The new criteria are applicable to both slow and fast time delays. Finally, numerical examples are given to illustrate the effectiveness of our theoretical results and less conservativeness of the proposed method.     

Fault gear identification and classification using discrete wavelet transform and adaptive neuro-fuzzy inference

In this paper, an intelligent diagnosis for fault gear identification and classification based on vibration signal using discrete wavelet transform and adaptive neuro-fuzzy inference system (ANFIS) is presented. The discrete wavelet transform (DWT) technique plays one of the important roles for signal feature extraction in the proposed system. The abnormal transient signals will show in different decomposition levels and can be used to recognize the various faults by the DWT figure. However, many fault conditions are hard to inspect accurately by the naked eye. In the present study, the feature extraction method based on discrete wavelet transform with energy spectrum is proposed. The different order wavelets are considered to identify fault features accurately. The database is established by feature vectors of energy spectrum which are used as input pattern in the training and identification process. Furthermore, the ANFIS is proposed to identify and classify the fault gear positions and the gear fault conditions in the fault diagnosis system. The proposed ANFIS includes both the fuzzy logic qualitative approximation and the adaptive neural network capability. The experimental results verified that the proposed ANFIS has more possibilities in fault gear identification. The ANFIS achieved an accuracy identification rate which was more satisfactory than traditional vision inspection in the proposed system.     

Characterisation and control of bubbling behaviour in gas–solid fluidised beds

Bubbling fluidised beds are often exploited for the good mixing of phases they promote, which is required in many chemical processes. However, the bubbles cause heterogeneity in the fluidised system, and their dynamics must be understood for good process control. In this paper, the dynamic behaviour of the bubbles in a planar gas–solid fluidised bed is described analytically and experimentally validated. The bed resembles a temporary buffer of gas for which the bubble residence time in the bed is important. Subsequently, a closed-loop controller was developed using visual feedback to regulate the process.     

基于流形学习和SVM的Web文档分类算法

为解决Web文档分类问题,提出一种基于流形学习和SVM的Web文档分类算法。该算法利用流形学习算法LPP对训练集中的高维Web文档空间进行非线性降维,从中找出隐藏在高维观测数据中有意义的低维结构,在降维后的低维特征空间中利用乘性更新规则的优化SVM进行分类预测。实验结果表明该算法以较少的运行时间获得更高的分类准确率。     

A new combination of artificial neural network and <Emphasis Type="Italic">K</Emphasis>-nearest neighbors models to predict blast-induced ground vibration and air-overpressure

Blasting operation is widely used method for rock excavation in mining and civil works. Ground vibration and air-overpressure (AOp) are two of the most detrimental effects induced by blasting. So, evaluation and prediction of ground vibration and AOp are essential. This paper presents a new combination of artificial neural network (ANN) and K-nearest neighbors (KNN) models to predict blast-induced ground vibration and AOp. Here, this combination is abbreviated using ANN-KNN. To indicate performance of the ANN-KNN model in predicting ground vibration and AOp, a pre-developed ANN as well as two empirical equations, presented by United States Bureau of Mines (USBM), were developed. To construct the mentioned models, maximum charge per delay (MC) and distance between blast face and monitoring station (D) were set as input parameters, whereas AOp and peak particle velocity (PPV), as a vibration index, were considered as output parameters. A database consisting of 75 datasets, obtained from the Shur river dam, Iran, was utilized to develop the mentioned models. In terms of using three performance indices, namely coefficient correlation (R ²), root mean square error and variance account for, the superiority of the ANN-KNN model was proved in comparison with the ANN and USBM equations.     

The application of interactive dynamic virtual surgical simulation visualization method

In this paper, an interactive dynamic simulation method is proposed to solve computational models of soft tissue undergoing large deformation, collision detection, and volume conservation in medical surgical simulation visualization. During the process of implementation of the interactive dynamic simulation method, the point-based method is used to simulate the elastic solids undergoing large deformations and the position-based method is used to simulate the objects collision, friction and volume conservation. Numerical results demonstrate that the proposed method improves the efficiency and stability of the response of heterogeneous soft tissue undergoing contact or even the multi-organs interactions, and it can be extended to interactive biopsy and cutting simulation.

     

An extension of fuzzy TOPSIS for a group decision making with an application to tehran stock exchange

In financial markets, investors attempt to maximize their profits within a constructed portfolio with the aim of optimizing the tradeoffs between risk and return across the many stocks. This requires proper handling of conflicting factors, which can benefit from the domain of multiple criteria decision making (MCDM). However, the indexes and factors representing the stock performance are often imprecise or vague and this should be represented by linguistic terms characterized by fuzzy numbers. The aim of this research is to first develop three group MCDM methods, then use them for selecting undervalued stocks by dint of financial ratios and subjective judgments of experts. This study proposes three versions of fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Solution): conventional TOPSIS (C-TOPSIS), adjusted TOPSIS (A-TOPSIS) and modified TOPSIS (M-TOPSIS) where a new fuzzy distance measure, derived from the confidence level of the experts and fuzzy performance ratings have been included in the proposed methods. The practical aspects of the proposed methods are demonstrated through a case study in the Tehran stock exchange (TSE), which is timely given the need for investors to select undervalued stocks in untapped markets in the anticipation of easing economic sanctions from a change in recent government leadership.     

Hypercolumn-array based image representation and its application to shape-based object detection

Biological and psychological evidence increasingly reveals that high-level geometrical and topological features are the keys to shape-based object recognition in the brain. Attracted by the excellent performance of neural visual systems, we simulate the mechanism of hypercolumns in the mammalian cortical area V1 that selectively responds to oriented bar stimuli. We design an orderly-arranged hypercolumn array to extract and represent linear or near-linear stimuli in an image. Each unit of this array covers stimuli of various orientations in a small area, and multiple units together produce a low-dimensional vector to describe shape. Based on the neighborhood of units in the array, we construct a graph whose node represents a short line segment with a certain position and slope. Therefore, a contour segment in the image can be represented with a route in this graph. The graph converts an image, comprised of typically unstructured raw data, into structured and semantic-enriched data. We search along the routes in the graph and compare them with a shape template for object detection. The graph greatly upgrades the level of image representation, remarkably reduces the load of combinations, significantly improves the efficiency of object searching, and facilitates the intervening of high-level knowledge. This work provides a systematic infrastructure for shape-based object recognition.     

强化狼群等级制度的灰狼优化算法

针对灰狼优化(Grey wolf optimization, GWO)算法在处理复杂优化问题时优化精度不高,易陷于局部最优等问题,提出了一种强化狼群等级制度的灰狼优化(GWO based on strengthening the hierarchy of wolves, GWOSH)算法。该算法为灰狼个体设置了跟随狩猎和自主探索两种狩猎模式,并根据自身等级情况来控制选择狼群的狩猎模式。在跟随狩猎模式中,灰狼个体以等级高于自身的灰狼的位置信息来指引自己到达最优解区域;而在自主探索模式中,灰狼个体会同时审视等级高于自身的灰狼的位置信息和自身位置信息,并基于这些信息自主判断猎物的位置,同时两种更新模式都将引入优胜劣汰选择规则来确保种群的狩猎方向。对12个基准测试函数进行优化的结果表明：与已有的算法相比,GWOSH算法的全局搜索能力更强,更能有效避免易早熟收敛的问题,更适用于求解高维的复杂优化问题。