An Internet-based negotiation server for e-commerce

This paper describes the design and implementation of a replicable, Internet-based negotiation server for conducting bargaining-type negotiations between enterprises involved in e-commerce and e-business. Enterprises can be buyers and sellers of products/services or participants of a complex supply chain engaged in purchasing, planning, and scheduling. Multiple copies of our server can be installed to complement the services of Web servers. Each enterprise can install or select a trusted negotiation server to represent his/her interests. Web-based GUI tools are used during the build-time registration process to specify the requirements, constraints, and rules that represent negotiation policies and strategies, preference scoring of different data conditions, and aggregation methods for deriving a global cost-benefit score for the item(s) under negotiation. The registration information is used by the negotiation servers to automatically conduct bargaining type negotiations on behalf of their clients. In this paper, we present the architecture of our implementation as well as a framework for automated negotiations, and describe a number of communication primitives which are used in the underlying negotiation protocol. A constraint satisfaction processor (CSP) is used to evaluate a negotiation proposal or counterproposal against the registered requirements and constraints of a client company. In case of a constraint violation, an event is posted to trigger the execution of negotiation strategic rules, which either automatically relax the violated constraint, ask for human intervention, invoke an application, or perform other remedial operations. An Event-Trigger-Rule (ETR) server is used to manage events, triggers, and rules. Negotiation strategic rules can be added or modified at run-time. A cost-benefit analysis component is used to perform quantitative analysis of alternatives. The use of negotiation servers to conduct automated negotiation has been demonstrated in the context of an integrated supply chain scenario. Received: 30 October 2000 / Accepted: 12 January 2001 Published online: 2 August 2001     

A new criterion of delay-dependent asymptotic stability for Hopfield neural networks with time delay.

In this brief, the problem of global asymptotic stability for delayed Hopfield neural networks (HNNs) is investigated. A new criterion of asymptotic stability is derived by introducing a new kind of Lyapunov-Krasovskii functional and is formulated in terms of a linear matrix inequality (LMI), which can be readily solved via standard software. This new criterion based on a delay fractioning approach proves to be much less conservative and the conservatism could be notably reduced by thinning the delay fractioning. An example is provided to show the effectiveness and the advantage of the proposed result.     

Per-queue stability analysis of a random access system

We have extended our previous studies (2000) of the system stability of buffered ALOHA systems to study an individual queue's stability, i.e., per-queue stability. The main result obtained in this work is a necessary and sufficient per-queue stability condition, which can be computed analytically only for several cases. For other incomputable cases, we evaluated several inner and outer bounds. They are generally quite tight for not-so-asymmetric systems     

T c enhancement in superconductor-semiconductor arrays

Subsequent to our observation that the Tl- and Bi-based cuprate high-T _c superconductors are built of superconductor-semiconductor arrays (P. C. W. Fung and W. Y. Kwok,J. Superconduct., this issue), we investigate in this paper the possibility ofT _c enhancement arising from the effect of change of phonon spectrum and the effect of size quantization when one or more semiconductor blocks is attached to the basic superconductor in the unit cell.     

Phase I IASC-ASCE Structural Health Monitoring Benchmark Problem Using Simulated Data

Structural health monitoring (SHM) is a promising field with widespread application in civil engineering. Structural health monitoring has the potential to make structures safer by observing both long-term structural changes and immediate postdisaster damage. However, the many SHM studies in the literature apply different monitoring methods to different structures, making side-by-side comparison of the methods difficult. This paper details the first phase in a benchmark SHM problem organized under the auspices of the IASC–ASCE Structural Health Monitoring Task Group. The scale-model structure adopted for use in this benchmark problem is described. Then, two analytical models based on the structure—one a 12 degree of freedom (DOF) shear-building model, the other a 120-DOF model, both finite element based—are given. The damage patterns to be identified are listed as well as the types and number of sensors, magnitude of sensor noise, and so forth. MATLAB computer codes to generate the response data for the various cases are explained. The codes, as well as details of the ongoing Task Group activities, are available on the Task Group web site at 〈http://wusceel.cive.wustl.edu/asce.shm/〉.     

Dynamic construction site layout planning using max-min ant system

Construction site layout planning (CSLP) is a dynamic multi-objective optimization (MOO) problem as there are different facilities employed in the different construction phases of a construction project. In this study, a new method using continuous dynamic searching scheme to guide the max-min ant system (MMAS) algorithm, which is one of the ant colony optimization (ACO) algorithms, to solve the dynamic CSLP problem under the two congruent objective functions of minimizing safety concerns and reducing construction cost is proposed. Using weighted sum method the MOO problem can be solved by the proposed MMAS method. An office building case was used to verify the capability of the proposed method to solve dynamic CSLP problem and the results are promising. The approach could be benchmarked by researchers using other advanced optimization algorithms to solve the same problem or expand the applications to other fields.     

Calculation of Mueller matrices and polarization signatures for aslab of random medium using vector radiative transfer

The Mueller matrix which characterizes a slab of random medium containing spherical particles is calculated by using the vector radiative transfer theory. The vector radiative transfer equation is solved for arbitrarily polarized incident waves. The background refractive index of the slab is allowed to be different from the surrounding media. The scattering specific intensities for four independent polarized incident waves are calculated and used to construct the Mueller matrix, which contains multiple scattering due to the randomly distributed particles governed by the vector radiative transfer theory. The calculated are found to be symmetrical, and there are eight nonvanishing matrix elements. Polarization signatures are obtained in the backscattering direction from the Mueller matrix of the reflection side     

Accelerating Machine-Learning Algorithms on FPGAs using Pattern-Based Decomposition

Machine-learning algorithms are employed in a wide variety of applications to extract useful information from data sets, and many are known to suffer from super-linear increases in computational time with increasing data size and number of signals being processed (data dimension). Certain principal machine-learning algorithms are commonly found embedded in larger detection, estimation, or classification operations. Three such principal algorithms are the Parzen window-based, non-parametric estimation of Probability Density Functions (PDFs), K-means clustering and correlation. Because they form an integral part of numerous machine-learning applications, fast and efficient execution of these algorithms is extremely desirable. FPGA-based reconfigurable computing (RC) has been successfully used to accelerate computationally intensive problems in a wide variety of scientific domains to achieve speedup over traditional software implementations. However, this potential benefit is quite often not fully realized because creating efficient FPGA designs is generally carried out in a laborious, case-specific manner requiring a great amount of redundant time and effort. In this paper, an approach using pattern-based decomposition for algorithm acceleration on FPGAs is proposed that offers significant increases in productivity via design reusability. Using this approach, we design, analyze, and implement a multi-dimensional PDF estimation algorithm using Gaussian kernels on FPGAs. First, the algorithm’s amenability to a hardware paradigm and expected speedups are predicted. After implementation, actual speedup and performance metrics are compared to the predictions, showing speedup on the order of 20× over a 3.2 GHz processor. Multi-core architectures are developed to further improve performance by scaling the design. Portability of the hardware design across multiple FPGA platforms is also analyzed. After implementing the PDF algorithm, the value of pattern-based decomposition to support reuse is demonstrated by rapid development of the K-means and correlation algorithms.