Distinct element method analysis of a retaining wall using a steel frame and fill materials

A technique is developed to clearly establish the shear resistance of a cellular structure, retaining wall composed of a steel frame and fill materials with both continuous and discontinuous characteristics. To overcome the limitation of the existing analysis approach based on continuum mechanics, in which the shear behavior and interaction between the frames and fill material of this type of structure are difficult to describe, this paper introduces displacement incremental analysis into the distinct element method. The results obtained by using the proposed approach are compared with experimental results to verify its accuracy. The results show an internal friction angle of fill materials and overburdening load are major factor determining the shear resistance of a retaining wall with a cellular structure type. From the results of the parametric study on the shear behavior of this type of structure, this paper also proposes a shear resistance moment-shear displacement formula for designing a retaining wall with a cellular structure type.     

安博士再推多款专车专用隔音套餐

在国内的汽车隔音市场上．安博士无疑是最勤奋．也最具创新精神的品牌了,其独辟蹊径推出的专车专用隔音概念．不仅赢得了市场上的一致好评．而且也获得了消费者的认可。     

Development of an operational fitness test for the Royal Air Force

Since 2002, the Royal Air Force (RAF) has been working towards developing role-related physical tests for use as an operational fitness test (OFT). The purpose of this study was to establish reliability of the OFT (comprising four tests), investigate gym-based tests as predictors of performance and establish performance standards. Fifty-eight RAF personnel performed the OFT on three occasions. A separate cohort carried out fitness and anthropometric tests before performing the OFT, by way of establishing performance predictors. Documented evidence and views of an expert panel were used to determine OFT standards. Reliability ranged from moderate to good for three tests, with one test (Dig) showing poor reliability. The 95% limits of agreement for the prediction models ranged from good to poor (6.7-34.2%). The prediction models were not sufficiently accurate to estimate confidently OFT performance, but could be used as a guide to quantify likely outcome and training needs.     

Unsupervised feature selection using clustering ensembles and population based incremental learning algorithm

This paper describes a novel feature selection algorithm for unsupervised clustering, that combines the clustering ensembles method and the population based incremental learning algorithm. The main idea of the proposed unsupervised feature selection algorithm is to search for a subset of all features such that the clustering algorithm trained on this feature subset can achieve the most similar clustering solution to the one obtained by an ensemble learning algorithm. In particular, a clustering solution is firstly achieved by a clustering ensembles method, then the population based incremental learning algorithm is adopted to find the feature subset that best fits the obtained clustering solution. One advantage of the proposed unsupervised feature selection algorithm is that it is dimensionality-unbiased. In addition, the proposed unsupervised feature selection algorithm leverages the consensus across multiple clustering solutions. Experimental results on several real data sets demonstrate that the proposed unsupervised feature selection algorithm is often able to obtain a better feature subset when compared with other existing unsupervised feature selection algorithms.     

Static virtualization of C source code

Many existing systems are written in C and are not re‐entrant or thread safe. Sometimes these systems are required in a context for which they were not first designed, possibly meaning they now need to be re‐entrant. This article introduces a program that filters C source code, modifying shared resources (the global variables) to make the code re‐entrant: ‘virtualizing’ the code. The code is then compiled as normal. This approach allows programmatic virtualization with little cost at runtime. Copyright © 2007 John Wiley & Sons, Ltd.     

Robust Adaptive Control of Cooperating Mobile Manipulators With Relative Motion

In this paper, coupled dynamics are presented for two cooperating mobile robotic manipulators manipulating an object with relative motion in the presence of uncertainties and external disturbances. Centralized robust adaptive controls are introduced to guarantee the motion, and force trajectories of the constrained object converge to the desired manifolds with prescribed performance. The stability of the closed-loop system and the boundedness of tracking errors are proved using Lyapunov stability synthesis. The tracking of the constraint trajectory/force up to an ultimately bounded error is achieved. The proposed adaptive controls are robust against relative motion disturbances and parametric uncertainties and are validated by simulation studies.     

An improved version of ISICS: A program for calculating K-, L- and M-shell cross sections from PWBA and ECPSSR theory using a personal computer

The formatting of the M-shell atomic parameters imbedded in file XCSC.H in ISICS has been corrected. The problem only affected cross section calculations for Uranium and heavier elements. The corrected version of ISICS has been re-compiled and is now available.

New version program summary

Program title: ISICSCatalogue identifier: ADDS_v3_0Program summary URL:http://cpc.cs.qub.ac.uk/summaries/ADDS_v3_0.htmlProgram obtainable from: CPC Program Library, Queen's University, Belfast, N. IrelandLicensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.htmlNo. of lines in distributed program, including test data, etc.: 4645No. of bytes in distributed program, including test data, etc.: 106 731Distribution format: tar.gzProgramming language: C++Computer: 80486 or higher-level PCsOperating system: WINDOWS 98 through WINDOWS XPClassification: 16.7Does the new version supersede the previous version?: YesNature of problem: Ionization and X-ray production cross section calculations for ion-atom collisions.Solution method: Numerical integration of form factor using a logarithmic transform and Gaussian quadrature, plus exact integration limits.Reasons for new version: The formatting of the M-shell atomic parameters involving cross section calculations for Uranium and heavier elements needed to be corrected.Summary of revisions: The affected file XCSC.H in ISICS has been corrected and ISICS has been recompiled.Restrictions: The consumed CPU time increases with the atomic shell (K, L, M), but execution is still very fast.Running time: This depends on which shell and the number of different energies to be used in the calculation. For example, to calculate K-shell cross sections for protons striking carbon for 19 different proton energies it took less than 10 s; to calculate M-shell cross sections for protons on gold for 21 proton energies it took 4.2 min.     

Neural network classifier and multiple hypothesis image segmentation for dietary assessment using calorie calculator

Nowadays, dietary assessment becomes the emerging system for evaluating the person’s food intake. In this paper, the multiple hypothesis image segmentation and feed-forward neural network classifier are proposed for dietary assessment to enhance the performance. Initially, the segmentation is applied to input image which is used to determine the regions where a particular food item is located using salient region detection, multi-scale segmentation, and fast rejection. Then, the significant feature of food items is extracted by the global feature and local feature extraction method. After the features are obtained, the classification is performed for each segmented region using feed-forward neural network model. Finally, the calorie value is computed with the aid of (i) food area volume and (ii) calorie and nutrition measure based on mass value. The outcome of the proposed method attains 96% of accuracy value which provides the better classification performance.     

The convergence analysis and specification of the Population-Based Incremental Learning algorithm

In this paper, we investigate the global convergence properties in probability of the Population-Based Incremental Learning (PBIL) algorithm when the initial configuration p⁽⁰⁾ is fixed and the learning rate α is close to zero. The convergence in probability of PBIL is confirmed by the experimental results. This paper presents a meaningful discussion on how to establish a unified convergence theory of PBIL that is not affected by the population and the selected individuals.