9193368
Economic and financial prediction using rough sets model
Abstract A state-of-the-art review of the literature related to economic and financial prediction using rough sets model is presented, with special emphasis on the business failure prediction, database marketing and financial investment. These three applications require the accurate prediction of the future states based on the identification of patterns in the historical data. In addition, the historical data are in the format of a multi-attribute information table. All these conditions suit the rough sets model, an effective tool for multi-attribute classification problems. The different rough sets models and issues concerning the implementation of rough sets model – indicator selection, discretization and validation test, are also discussed in this paper. This paper will demonstrate that rough sets model is applicable to a wide range of practical problems pertaining to economic and financial prediction. In addition, the results show that the rough sets model is a promising alternative to the conventional methods for economic and financial prediction.
811735
Classifying Market States with WARS
In this paper, a new indicator - WARS (Weighted Accumulated Reconstruction Series) at classifying the state of financial market, either trending state or mean-reverting state, was presented. Originated from the computation of Entropy, this new indicator was found to be able to reflect the market behavior accurately and easily. The algorithm of generating WARS and its meaning related to Entropy were introduced and some comparison results between WARS and the Daily Profit Curve were listed. As a new indicator, WARS also can be used to build a trading system - to provide buy, sell and hold signals. Through the application on S&P 500 index, it was verified to be effective and was a promising indicator.
1043193
Dependability Prediction of High Availability OSCAR Cluster Server
High availability (HA) computing has recently gained much attention, especially in enterprise and mission critical systems. The HA is now a necessity that is no longer regarded as a luxury feature. Thus, we, conjunctively with the open source community, are in process of enhancing the HA feature to Open Source Cluster Application Resources (OSCAR), a widely adopted Linux PC cluster system. Server redundancy will be our initial key aspect of the next generation HA OSCAR cluster system. In this paper, we introduce a HA server for OSCAR cluster system. Its architecture and mechanism is discussed, and then we model and predict the dependability of the system by a Petri net-based model, Stochastic Reword Net (SRN). The reliability and instantaneous availability of the system are presented as a result.
13929087
Availability prediction and modeling of high mobility OSCAR cluster
Since the initial introduction of open source cluster application resources (OSCAR), this software package has been a well-accepted choice for building high performance computing systems. As it continues to be applied to mission-critical environments, high availability (HA) features therefore are needed to be included in OSCAR cluster. In this paper, we provide a HA solution for OSCAR cluster. As a widely used technique in HA solutions, component redundancy is adopted to improve the system availability. Based on the proposed architecture, we develop a detailed failure-repair model for predicting the availability of HA OSCAR cluster. stochastic reward nets (SRN) are used to model the behavior of the system. We specify our SRN model to stochastic Petri net package, describe and compute several interesting output measures that characterize availability features of HA OSCAR cluster.
31684732
Achieving high availability and performance computing with an HA-OSCAR cluster
High availability (HA) computing has long gained much attention in enterprise and mission critical systems. HA goals are to maximize the uptime, thus undoubtedly complementing high-performance computing (HPC) objectives. HA-OSCAR is a project that aims to improve HA in commercial-off-the-shelf (COTS)-based HPC environments. In this paper, we introduce a multi-head HPC cluster architecture. Server redundancy is an initial key aspect aiming toward downtime reduction. Two HA-OSCAR types, active-standby and active-active, are studied. We evaluate system dependability for given two models. Stochastic Reward Nets (SRN) are used to model the system availability. We describe our SRN modeling using Stochastic Petri Net Package, and compute several interesting results that characterize HA-OSCAR availability.
635773
Surface Alignment of 3D Spherical Harmonic Models: Application to Cardiac MRI Analysis
The spherical harmonic (SPHARM) description is a powerful surface modeling technique that can model arbitrarily shaped but simply connected 3D objects and has been used in many applications in medical imaging. Previous SPHARM techniques use the first order ellipsoid for establishing surface correspondence and aligning objects. However, this first order information may not be sufficient in many cases; a more general method for establishing surface correspondence would be to minimize the mean squared distance between two corresponding surfaces. In this paper, a new surface matching algorithm is proposed for 3D SPHARM models to achieve this goal. This algorithm employs a useful rotational property of spherical harmonic basis functions for a fast implementation. Applications of medical image analysis (e.g., spatio-temporal modeling of heart shape changes) are used to demonstrate this approach. Theoretical proofs and experimental results show that our approach is an accurate and flexible surface correspondence alignment method.
6802677
A surface-based approach for classification of 3D neuroanatomic structures
We present a new framework for 3D surface object classification that combines a powerful shape description method with suitable pattern classification techniques. Spherical harmonic parameterization and normalization techniques are used to describe a surface shape and derive a dual high dimensional landmark representation. A point distribution model is applied to reduce the dimensionality. Fisher's linear discriminants and support vector machines are used for classification. Several feature selection schemes are proposed for learning better classifiers. After showing the effectiveness of this framework using simulated shape data, we apply it to real hippocampal data in schizophrenia and perform extensive experimental studies by examining different combinations of techniques. We achieve best leave-one-out cross-validation accuracies of 93% (whole set, N = 56) and 90% (right-handed males, N = 39), respectively, which are competitive with the best results in previous studies using different techniques on similar types of data. Furthermore, to help medical diagnosis in practice, we employ a threshold-free receiver operating characteristic (ROC) approach as an alternative evaluation of classification results as well as propose a new method for visualizing discriminative patterns.