342298
Nonlinear information aggregation via evidential reasoning in multiattribute decision analysis under uncertainty
In many decision situations, it is inevitable to deal with both quantitative and qualitative information under uncertainty. Evidence-based reasoning within a multiple criteria decision analysis framework provides an alternative way of handling such information systematically and consistently. In this paper, the evidential reasoning (ER) approach is introduced, which is based on a recursive ER algorithm that, in essence, constitutes a nonlinear information aggregation process. To facilitate the application of the ER approach and as an indispensable part of its development, the nonlinear features of the ER information aggregation process need to be thoroughly investigated and properly understood. This forms the theme of this paper where the nonlinear features are explored by examining typical reasoning patterns in aggregating harmonic, quasi-harmonic, and contradictory decision information. This analytical investigation provides insights into the recursive nature of the ER approach as well as valuable experience that could be useful to other researchers and practitioners interested in developing and applying operation research/artificial intelligence (OR/AI)-based approaches for decision analysis under uncertainty. The analytical study is complemented by the numerical studies of two application examples. The analysis of a quality assessment problem for motor engines is aimed to show the step-by-step process of implementing the ER approach and to illustrate its nonlinear features in a real-life decision situation. The study of a more complex assessment problem in ship design is intended to demonstrate the potential of the ER approach and its supporting software for dealing with general decision problems.
2868377
Intelligent decision system and its application in business innovation self assessment
In this paper, it is described how a multiple criteria decision analysis software tool, the Intelligent Decision System (IDS), can be used to help business self-assessment. Following a brief outline of a model for assessing business innovation capability and the IDS software, the process of using IDS to implement different types of assessment questions is discussed. It is demonstrated that IDS is a flexible tool capable of handling different types of data in self-assessment, including uncertain and incomplete data, and providing a wide range of information including scores, performance diversity, strength and weakness profile and graphics.
15521655
An ultra-fast shared path protection scheme - distributed partial information management, part II
For pt.I see Chunming Qiao and Dahai Xu, INFOCOM'02, p.302-11, (2002). This paper describes a novel, ultra-fast heuristic algorithm to address an NP-hard optimization problem. One of its significances is that, for the first time, it is shown that a heuristic algorithm can also have better overall performance than its time-consuming, integer linear programming (ILP) based counterparts in the online case, which is non-intuitive. The proposed heuristic algorithm is useful for developing effective shared path (mesh) protection schemes that establish survivable connections in modern networks. The advantage of our heuristic algorithm over existing algorithms for finding a pair of link (or node) disjoint paths, active path (AP) and backup path (BP), comes from the following salient feature. It uses a so-called potential backup cost (PBC) function when selecting an AP in the first phase, in order to take into consideration the backup bandwidth needed by the corresponding BP yet to be chosen in the second phase. The PBC function is derived mathematically based on a rigorous statistical analysis of experimental data. While the use of PBC only requires partial aggregate information on existing connections and distributed control, it can also be applied even more effectively when complete information is available.
18153606
Virtual distributed environments in a shared infrastructure
We have developed a middleware system that integrates and extends virtual machine and virtual network technologies to support mutually isolated virtual distributed environments in shared infrastructures like the grid and the PlanetLab overlay infrastructure. Integrating virtual network and on-demand virtual machine creation and customization technologies makes virtual distributed environments a reality. The Violin-based middleware system integrates and enhances such technologies to create virtual distributed environments.
8230807
A Petri Net Based Software Architecture for UAV Simulation
This paper presents a Petri net-based hierarchical architecture for multiple Unmanned Aerial Vehicle (UAV) simulations in order to facilitate rapid prototyping via visual modeling and analysis. Using high-level Colored Petri nets, we specify UAV control structure by a hierarchy of mission control net and task nets: the mission control net, built upon task nets, models the decision making process of the UAV simulation; tasks nets are built from primitive UAV behaviors and environmental behaviors. To realize effects of UAV behaviors on the simulation environment, procedures may be associated with transitions in Petri nets. To demonstrate the approach, we describe two experimental UAV mission scenarios: sweeping search and multi-point attack.
8195983
Poisson shape interpolation
In this paper, we propose a novel shape interpolation approach based on Poisson equation. We formulate the trajectory problem of shape interpolation as solving Poisson equations defined on a domain mesh. A non-linear gradient field interpolation method is proposed to take both vertex coordinates and surface orientation into account. With proper boundary conditions, the in-between shapes are reconstructed implicitly from the interpolated gradient fields, while traditional methods usually manipulate vertex coordinates directly. Besides of global shape interpolation, our method is also applicable to local shape interpolation, and can be further enhanced by incorporating with deformation. Our approach can generate visual pleasing and physical plausible morphing sequences with stable area and volume changes. Experimental results demonstrate that our technique can avoid the shrinkage problem appeared in linear shape interpolation.
887820
A computational method for assessing peptide-identification reliability in tandem mass spectrometry analysis with SEQUEST
High throughput protein identification in mass spectrometry is predominantly achieved by first identifying tryptic peptides using SEQUEST and then by combining the peptide hits for protein identification. Peptide identification is typically carried out by selecting SEQUEST hits above a specified threshold, the value of which is typically chosen empirically in an attempt to separate true identifications from the false ones. These SEQUEST scores are not normalized with respect to the composition, length and other parameters of the peptides. Furthermore, there is no rigorous reliability estimate assigned to the protein identifications derived from these scores. Hence the interpretation of SEQUEST hits generally requires human involvement, making it difficult to scale up the identification process for genome-scale applications. To overcome these limitations, we have developed a method, which combines a neural network and a statistical model, for "normalizing" SEQUEST scores, and also for providing a reliability estimate for each SEQUEST hit. This method improves the sensitivity and specificity of peptide identification compared to the standard filtering procedure used in the SEQUEST package, and provides a basis for estimating the reliability of protein identifications.