Identification of statistical patterns in complex systems via symbolic time series analysis

Identification of statistical patterns from observed time series of spatially distributed sensor data is critical for performance monitoring and decision making in human-engineered complex systems, such as electric power generation, petrochemical, and networked transportation. This paper presents an information-theoretic approach to identification of statistical patterns in such systems, where the main objective is to enhance structural integrity and operation reliability. The core concept of pattern identification is built upon the principles of Symbolic Dynamics, Automata Theory, and Information Theory. To this end, a symbolic time series analysis method has been formulated and experimentally validated on a special-purpose test apparatus that is designed for data acquisition and real-time analysis of fatigue damage in polycrystalline alloys.     

Introducing robustness in multi-objective optimization

In optimization studies including multi-objective optimization, the main focus is placed on finding the global optimum or global Pareto-optimal solutions, representing the best possible objective values. However, in practice, users may not always be interested in finding the so-called global best solutions, particularly when these solutions are quite sensitive to the variable perturbations which cannot be avoided in practice. In such cases, practitioners are interested in finding the robust solutions which are less sensitive to small perturbations in variables. Although robust optimization is dealt with in detail in single-objective evolutionary optimization studies, in this paper, we present two different robust multi-objective optimization procedures, where the emphasis is to find a robust frontier, instead of the global Pareto-optimal frontier in a problem. The first procedure is a straightforward extension of a technique used for single-objective optimization and the second procedure is a more practical approach enabling a user to set the extent of robustness desired in a problem. To demonstrate the differences between global and robust multi-objective optimization principles and the differences between the two robust optimization procedures suggested here, we develop a number of constrained and unconstrained test problems having two and three objectives and show simulation results using an evolutionary multi-objective optimization (EMO) algorithm. Finally, we also apply both robust optimization methodologies to an engineering design problem.     

An Efficient Output-Size Sensitive Parallel Algorithm for Hidden-Surface Removal for Terrains

We describe an efficient parallel algorithm for hidden-surface removal for terrain maps. The algorithm runs in O(log ⁴ n) steps on the CREW PRAM model with a work bound of O((n+k) \polylog ( n)) where n and k are the input and output sizes, respectively. In order to achieve the work bound we use a number of techniques, among which our use of persistent data structures is somewhat novel in the context of parallel algorithms. Received July 29, 1998; revised October 5, 1999.     

Polynomial modeling and reduction of RF body coil spatial inhomogeneity in MRI

The usefulness of statistical clustering algorithms developed for automatic segmentation of lesions and organs in magnetic resonance imaging (MRI) intensity data sets suffers from spatial nonstationarities introduced into the data sets by the acquisition instrumentation. The major intensity inhomogeneity in MRI is caused by variations in the B1-field of the radio frequency (RF) coil. A three-step method was developed to model and then reduce the effect. Using a least squares formulation, the inhomogeneity is modeled as a maximum variation order two polynomial. In the log domain the polynomial model is subtracted from the actual patient data set resulting in a compensated data set. The compensated data set is exponentiated and rescaled. Statistical comparisons indicate volumes of significant corruption undergo a large reduction in the inhomogeneity, whereas volumes of minimal corruption are not significantly changed. Acting as a preprocessor, the proposed technique can enhance the role of statistical segmentation algorithms in body MRI data sets.     

The PROMPT real-time commit protocol

We investigate the performance implications of providing transaction atomicity for firm-deadline real-time applications, operating on distributed data. Using a detailed simulation model, the real-time performance of a representative set of classical transaction commit protocols is evaluated. The experimental results show that data distribution has a significant influence on real-time performance and that the choice of commit protocol clearly affects the magnitude of this influence. We also propose and evaluate a new commit protocol, PROMPT (Permits Reading Of Modified Prepared-data for Timeliness), that is specifically designed for the real-time domain. PROMPT allows transactions to “optimistically” borrow, in a controlled manner, the updated data of transactions currently in their commit phase. This controlled borrowing reduces the data inaccessibility and the priority inversion that is inherent in distributed real-time commit processing. A simulation-based evaluation shows PROMPT to be highly successful, as compared to the classical commit protocols, in minimizing the number of missed transaction deadlines. In fact, its performance is close to the best on-line performance that could be achieved using the optimistic lending approach. Further, it is easy to implement and incorporate in current database system software. Finally, PROMPT is compared against an alternative priority inheritance-based approach to addressing priority inversion during commit processing. The results indicate that priority inheritance does not provide tangible performance benefits     

A theoretical and empirical investigation of multi‐item on‐line auctions

In this paper we explore and analyze the structure of Internet auctions from an analytical and an empirical perspective. Such web‐based auctions are rapidly emerging as a mercantile process of choice in the electronic marketplace. We observe current Internet auctions for one‐time products, such as rapidly aging hardware, and analyze them within the framework of the existing auction theory. While traditional auction theory focuses on single‐item auctions, we observe that a majority of on‐line auctions are multi‐item auctions. A significant contribution of this work is the theoretical derivation of the structure of the winning bids in multi‐item progressive on‐line auctions. Additionally, for comparative purposes, we explore the structural characteristics of alternative multi‐item auction mechanisms proposed in the auction theory. We derive hypotheses based on our analytical results and compare two different types of auction mechanisms. We test the traditional auction theory assumption regarding the homogeneity of bidders and present the first ever empirically derived classification and performance‐comparison of on‐line bidders. We test our hypotheses using real‐world empirical data obtained by tracking a premier web‐based auction site. Statistical analysis of the data indicates that firms may gain by choosing alternative auction mechanisms. We also provide directions for further exploration of this emerging but important dimension of electronic commerce. This revised version was published online in July 2006 with corrections to the Cover Date.     

Modeling mineral phase change chemistry of groundwater in a rural-urban fringe

This research paper aims to determine the genetic origin of the chemical elements in groundwater. It deals with the results of physicochemical parameters, to evaluate the hydro-geochemistry of groundwater in rural-urban fringe of district Bareilly, India. Pre- and post-monsoon sampling has been carried out, which reveals inter-seasonal variability effect on the hydro-geochemical processes. Geochemical modeling especially computation of saturation index was undertaken using the WATEQ4F model. Majority of samples fall in the category of undersaturation, which further suggests that groundwater still has potential to dissolve more minerals. Chemical categorizations of groundwater samples were performed with the help of the Aquachem model. Grouping of groundwater on the Piper diagram reveals a common composition and origin. In most of the area, water facies is of Ca(2+)-HCO(3)(-) type in both the seasons. It also indicates that in pre-monsoon, ion exchange is the dominant process, whereas in post-monsoon, both ion exchanges as well as reverse ion exchanges are reported in the groundwater of the study area.     

Palmprint based recognition system using phase-difference information

This paper presents an efficient palmprint based human recognition system. Each palmprint is divided into several square overlapping blocks. Reconstruction error using principle component analysis (PCA) is used to classify these blocks into either a good block or a non-palmprint block. Features from each good block of a palmprint are obtained by binarising the phase-difference of vertical and horizontal phase. The Hamming distance is used to compute the matching score between the features of corresponding good blocks of enrolled and live palmprint. These matching scores are fused using weighted sum rule, where weights are based on the average discriminating level of a block relative to other blocks. The performance of the proposed system is analysed on different datasets of hand images and it has been observed that it achieves a Correct Recognition Rate of 100% with a low Equal Error Rate for all the datasets. The system is also evaluated for noisy and bad palmprint images. It is found to be robust as long as the noise density is less than 50% or the bad region is less than 64% of the images.