Parallel optimization of three-dimensional wedge-shaped underwater acoustic propagation based on MPI+OpenMP hybrid programming model

The three-dimensional wedge-shaped underwater acoustic propagation model exists analytical solution, which provides verification for models like FOR3D propagation model under certain situation. However, the solving process of a three-dimensional complex underwater sound field problem is hindered by intensive computing and long calculation times. In this paper, we exploit a hybrid parallel programing model, such as MPI and OpenMP, to accelerate the computation, design various optimization methods to improve the overall performance, and then carry out the performance and optimization analysis on the Tianhe-2 platform. Experiments show that the optimized implementation of the three-dimensional wedge-shaped underwater acoustic propagation model achieves a 46.5 speedup compared to the original serial program, thereby illustrating a substantial performance improvement. We also carried out scalability tests and parallel optimization experiments for large-scale practical examples.

     

Enhanced dynamic credential generation scheme for protection of user identity in mobile-cloud computing

To improve the resource limitation of mobile devices, mobile users may utilize cloud-computational and storage services. Although the utilization of the cloud services improves the processing and storage capacity of mobile devices, the migration of confidential information on untrusted cloud raises security and privacy issues. Considering the security of mobile-cloud-computing subscribers’ information, a mechanism to authenticate legitimate mobile users in the cloud environment is sought. Usually, the mobile users are authenticated in the cloud environment through digital credential methods, such as password. Once the users’ credential information theft occurs, the adversary can use the hacked information for impersonating the mobile user later on. The alarming situation is that the mobile user is unaware about adversary’s malicious activities. In this paper, a light-weight security scheme is proposed for mobile user in cloud environment to protect the mobile user’s identity with dynamic credentials. The proposed scheme offloads the frequently occurring dynamic credential generation operations on a trusted entity to keep minimum processing burden on the mobile device. To enhance the security and reliability of the scheme, the credential information is updated frequently on the basis of mobile-cloud packets exchange. Furthermore, the proposed scheme is compared with the existing scheme on the basis of performance metrics i.e. turnaround time and energy consumption. The experimental results for the proposed scheme showed significant improvement in turnaround time and energy consumption as compared to the existing scheme.     

Multisensor Fusion: An Autonomous Mobile Robot

A conventional autonomous mobile robot is introduced. The main idea is the integration of many conventional and sophisticated sensor fusion techniques, introduced by several authors in recent years. We show the actual possibility of integrating all these techniques together, rather than analyzing implementation details. The topics of multisensor fusion, observation integration and sensor coordination are widely used throuhout the article. The final goal is to demonstrate the validity of both mathematical and artificial intelligence techniques in guaranteeing vehicle survival in a dynamic environment, while the robot carries out a specific task. We review conventional techniques for the management of uncertainty while we describe an implementation of a mobile robot which combines on-line heterogeneous sensors in its navigation and localisation tasks.     

iMLP: Applying Multi-Layer Perceptrons to Interval-Valued Data

Interval-valued data offer a valuable way of representing the available information in complex problems where uncertainty, inaccuracy or variability must be taken into account. In addition, the combination of Interval Analysis with soft-computing methods, such as neural networks, have shown their potential to satisfy the requirements of the decision support systems when tackling complex situations. This paper proposes and analyzes a new model of Multilayer Perceptron based on interval arithmetic that facilitates handling input and output interval data, but where weights and biases are single-valued and not interval-valued. Two applications are considered. The first one shows an interval-valued function approximation model and the second one evaluates the prediction intervals of crisp models fed with interval-valued input data. The approximation capabilities of the proposed model are illustrated by means of its application to the forecasting of daily electricity price intervals. Finally, further research issues are discussed. Research funded by Universidad Pontificia Comillas.     

可伐合金与无氧铜复合材料的研制

采用外皮为可伐合金(4J29)内芯为无氧 Cu(UT_1),经热加工、冷加工和扩散退火等工艺复合制成的复合材料。Cu 芯是圆形的,复合界面形成固溶体并牢固地结合在一起。轴向平均线膨胀系数α_(20)—400℃=(4.9-5.5)×10~(-6)/℃。外径为1.5mm,内芯径为0.6mm 复合线,其电阻率ρ=8.3×10~(-8)Ω·m;复合界面漏气率小于6.67×10~(-8)Pa·L/S。     

The Price of Anarchy in Network Creation Games Is (Mostly) Constant

We study the price of anarchy and the structure of equilibria in network creation games. A network creation game is played by n players {1,2,…,n}, each identified with a vertex of a graph (network), where the strategy of player i, i=1,…,n, is to build some edges adjacent to i. The cost of building an edge is α>0, a fixed parameter of the game. The goal of every player is to minimize its creation cost plus its usage cost. The creation cost of player i is α times the number of built edges. In the SumGame variant, the usage cost of player i is the sum of distances from i to every node of the resulting graph. In the MaxGame variant, the usage cost is the eccentricity of i in the resulting graph of the game. In this paper we improve previously known bounds on the price of anarchy of the game (of both variants) for various ranges of α, and give new insights into the structure of equilibria for various values of α. The two main results of the paper show that for α>273?n all equilibria in SumGame are trees and thus the price of anarchy is constant, and that for α>129 all equilibria in MaxGame are trees and the price of anarchy is constant. For SumGame this answers (almost completely) one of the fundamental open problems in the field—is price of anarchy of the network creation game constant for all values of α?—in an affirmative way, up to a tiny range of α.     

Mental imagery classification using one-dimensional convolutional neural network for target selection in single-channel BCI-controlled mobile robot

Neural Computing and Applications - This paper introduces the use of the one-dimensional convolutional neural network (1D-CNN) for end-to-end EEG decoding with application towards a BCI system with...     

Programming with C++ concepts

This paper explores the definition, applications, and limitations of concepts and concept maps in C++, with a focus on library composition. We also compare and contrast concepts to adaptation mechanisms in other languages.Efficient, non-intrusive adaptation mechanisms are essential when adapting data structures to a library’s API. Development with reusable components is a widely practiced method of building software. Components vary in form, ranging from source code to non-modifiable binary libraries. The Concepts language features, slated to appear in the next version of C++, have been designed with such compositions in mind, promising an improved ability to create generic, non-intrusive, efficient, and identity-preserving adapters.We report on two cases of data structure adaptation between different libraries, and illustrate best practices and idioms. First, we adapt GUI widgets from several libraries, with differing APIs, for use with a generic layout engine. We further develop this example to describe the run-time concept idiom, extending the applicability of concepts to domains where run-time polymorphism is required. Second, we compose an image processing library and a graph algorithm library, by making use of a transparent adaptation layer, enabling the efficient application of graph algorithms to the image processing domain. We use the adaptation layer to realize a few key algorithms, and report little or no performance degradation.     

基于量子粒子群算法的聚类分析方法

针对K-均值聚类方法受初始聚类中心影响,容易陷入局部最优解的问题,提出一种基于量子粒子群算法的聚类方法,该方法引入了动态调整量子门旋转角和量子变异操作,采用改进的变异算子,使粒子群体保持品种的多样性和优良性,避免陷入局部最优,同时结合粒子群优化算法,增加粒子群的全局搜索能力。仿真实验表明该方法在全局寻优能力和收敛效率上都有所提高。     

A boosting approach for corporate failure prediction

Predicting corporate failure is an important management science problem. This is a typical classification question where the objective is to determine which indicators are involved in the failure/success of a corporation. Despite the importance of this problem, until now only classical machine learning tools have been considered to tackle this classification task. The objective of this paper is twofold. On the one hand, we introduce novel discerning measures to rank independent variables in a generic classification task. On the other hand, we apply boosting techniques to improve the accuracy of a classification tree. We apply this methodology to a set of European firms, considering the usual predicting variables such as financial ratios, as well as including novel variables rarely used before in corporate failure prediction, such as firm size, activity and legal structure. We show that our approach decreases the generalization error about thirty percent with respect to the error produced with a classification tree. In addition, the most important ratios deal with profitability and indebtedness, as is usual in failure prediction studies. E. A. Cortés · M. G. Martínez · N. G. Rubio. The authors teach Statistics at the Faculty of Economic and Business Sciences in the University of Castilla-La Mancha. Esteban Alfaro completed his degree in Business in 1999 and got his Ph.D. in Economics in 2005, both in the University of Castilla-La Mancha. His thesis dealt with the application of ensemble classifiers to corporate failure prediction. Matías Gámez got his degree in Mathematics at the University of Granada in 1991 and finished a Master in Applied Statistics a year after. He completed his Ph.D. in Economics at the University of Castilla-La Mancha in 1998 on the application of geo-statistical techniques to the estimation of housing prices. Noelia García got her degree in Economics at the University of Madrid (UAM) in 1996 and completed her Ph.D. in Economics in 2004 on the construction of an intelligent and automated system for property valuation through the combination of neural nets and a geographic information system (GIS). Current research deals with spatial statistics and the combination of classifiers (decision trees and neural nets) for solving heated topics in the Economics.