Mixed coordination mechanisms for scheduling games on hierarchical machines

In this paper, we study scheduling games under mixed coordination mechanisms on hierarchical machines. The two scheduling policies involved are ‐ and ‐, where ‐ (resp., ‐) policy sequences jobs in nondecreasing order of their hierarchies, and jobs of the same hierarchy in nonincreasing (resp., nondecreasing) order of their processing times. We first show the existence of a Nash equilibrium. Then we present the price of anarchy and the price of stability for the games with social costs of minimizing the makespan and maximizing the minimum machine load. All the bounds given in this paper are tight.     

An intelligent web-page classifier with fair feature-subset selection

The explosion of on-line information has given rise to many manually constructed topic hierarchies (such as Yahoo!!). But with the current growth rate in the amount of information, manual classification in topic hierarchies results in an immense information bottleneck. Therefore, developing an automatic classifier is an urgent need. However, classifiers suffer from enormous dimensionality, since the dimensionality is determined by the number of distinct keywords in a document corpus. More seriously, most classifiers are either working slowly or they are constructed subjectively without any learning ability. In this paper, we address these problems with a fair feature-subset selection (FFSS) algorithm and an adaptive fuzzy learning network (AFLN) for classification. The FFSS algorithm is used to reduce the enormous dimensionality. It not only gives fair treatment to each category but also has ability to identify useful features, including both positive and negative features. On the other hand, the AFLN provides extremely fast learning ability to model the uncertain behavior for classification so as to correct the fuzzy matrix automatically. Experimental results show that both FFSS algorithm and the AFLN lead to a significant improvement in document classification, compared to alternative approaches.     

A Neural Network Approach for Signal Detection in Digital Communications

In this paper, we propose a new approach for signal detection in wireless digital communications based on the neural network with transient chaos and time-varying gain (NNTCTG), and give a concrete model of the signal detector after appropriate transformations and mappings. It is well known that the problem of the maximum likelihood signal detection can be described as a complex optimization problem that has so many local optima that conventional Hopfield-type neural networks fail to solve. By refraining from the serious local optima problem of Hopfield-type neural networks, the NNTCTG makes use of the time-varying parameters of the recurrent neural network to control the evolving behavior of the network so that the network undergoes the transition from chaotic behavior to gradient convergence. It has richer and more flexible dynamics rather than conventional neural networks only with point attractors, so that it can be expected to have much ability to search for globally optimal or near-optimal solutions. After going through a transiently inverse-bifurcation process, the NNTCTG can approach the global optimum or the neighborhood of global optimum of our problem. Simulation experiments have been performed to show the effectiveness and validation of the proposed neural network based method for the signal detection in digital communications.     

锗γ能谱测量中的符合相加修正

本文介绍了锗γ能谱测量中符合相加修正的原理和方法,给出了修正公式。分别计算了不同几何条件下两个锗探测器对一些放射性核素的符合相加修正因子,估计了符合相加修正因子对活度测量结果的误差贡献。     

Sequential tripping strategy for a transmission network back-upprotection expert system

This paper describes a sequential tripping strategy used in a wide area back-up protection expert system (BPES) to combat situations in which protection relays have maloperated or information is missing. The BPES is an innovative back-up protection scheme designed to prevent the occurrence of widespread blackouts. The BPES evaluates the certainty that transmission lines are likely to be affected by the fault and uses a sequential tripping strategy to isolate the fault if a firm decision is not available due to maloperated relays and/or missing information. The mode of analysis and the sequential tripping strategy ensures that the BPES will clear a fault at minimum risk to the network. An example is included to demonstrate how the certainty factor based sequential tripping strategy is employed by the BPES to clear a fault which occurred on the South Western part of the UK National Grid System     

Parameters evaluation of SOLA algorithm for time scale modification

Successful operation of the Synchronous Overlap and Add (SOLA) algorithm for Time Scale Modification (TSM) of speech is closely tied to the proper choice of parameters. This paper investigates the quality of time scale modified speech under different values of primary parameters. Based on Mean Opinion Score (MOS) tests and Bark Spectral Distortion (BSD) measure, the proper choices of synthesis shift (Ss) and the duration of the shift search interval (K _max?) are given experimentally. The conclusions can be helpful for operating the SOLA algorithm for time scale modification of speech.     

ADI-FDTD Method With Fourth Order Accuracy in Time

This letter presents an unconditionally stable alternating direction implicit finite-difference time-domain (ADI-FDTD) method with fourth order accuracy in time. Analytical proof of unconditional stability and detailed analysis of numerical dispersion are presented. Compared to second order ADI-FDTD and six-steps SS-FDTD, the fourth order ADI-FDTD generally achieves lower phase velocity error for sufficiently fine mesh. Using finer mesh gridding also reduces the phase velocity error floor, which dictates the accuracy limit due to spatial discretization errors when the time step size is reduced further.     

Perceived risk and trust associated with purchasing at electronic marketplaces

Understanding consumer behaviour is of vital importance to consumer-oriented e-business models today. In this paper, we study the relationships between consumer perceptions of risk and trust and the attitude towards purchasing at a consumer-to-consumer electronic marketplace (EM). Typical for EM settings is that consumer behaviour is subject to perceptions of the selling party as well as of the institutional structures of the intermediary that is operating the EM. Building upon the well-established literature of trust, we consider the concepts of intermediary trust and seller trust. We extend this categorisation by introducing the concepts of intermediary risk and seller risk. We developed measurement instruments for intermediary risk and seller risk. All measurement scales have acceptable alphas and are unidimensional. An empirical study is conducted to explore the relationships between the risk and trust types and consumer purchase attitude. The results reveal significant, direct effects of seller trust and seller risk. Second-order effects of intermediary trust and intermediary risk are investigated and reported. The paper concludes with general observations and recommendations for research and practice.