34672891
Estimating Groundwater Pollution Source Location from Observed Breakthrough Curves Using Neural Networks
This paper presents the results of a study aimed at estimating groundwater pollution source location from observed breakthrough curves using neural networks. Two different methods of presenting the breakthrough curves to the ANN are investigated. The feed-forward multi-layer perceptron (MLP) type artificial neural network (ANN) models are employed. The ANNs were trained using the back-propagation training algorithm on simulated data. A new approach for ANN training using back-propagation is employed that considers two different error statistics to prevent over-training or under-training of the ANNs. The preliminary results indicate that the ANNs are very efficient tools for estimating the distance of the potential pollution source from the observation well where breakthrough curve is measured.
42762171
Short-term water demand forecasting using artificial neural networks: IIT Kanpur experience
In this paper the relatively new technique of artificial neural networks (ANNs) has been investigated for use in forecasting short-term water demand. Other methods investigated for comparison purposes include regression and time series analyses. The data employed in this study consist of the weekly water demand at the Indian Institute of technology (IIT) Kanpur campus, and the rainfall and maximum temperature from the City of Kanpur, India. The ANN models consistently outperformed the regression and time series models developed in this study. An average error in forecasting of 3.28% was achieved from the best ANN model. It was found that the water demand at IIT Kanpur is better correlated with the rainfall occurrence rather than the amount of rainfall.
18557608
Performance Paradox : Information Technology Investments and Administrative Performance in the Case of the 50 U.S. State Governments
This study introduces the terminology of performance paradox in the context of IT (information technology) investment and use. Although it is somewhat related to the concept of productivity paradox, in the case of performance paradox, the dependent variable (i.e., the measure of performance) is not productivity, but some other measure of performance. This paper presents the results of a study that examined the relationship between IT investments by the 50 state governments of the United States and the administrative performance of these state governments. There are two parts to this study. In the first part, IT investments by state governments were related to multiple measures of state government performance, namely performance in financial management, human resource management, information technology management, capital management, and managing for results. In the second part of the study, IT investments by state governments were related to projected state government budget deficits. State budget deficits are used as a performance measure because they are an indicator of state government planning performance. Structural equation modeling was performed to analyze the data. The results indicate incidence of performance paradox. It appears as though the more U.S. state governments invest in IT, the worse they perform. However, there are also indications that with the passage of time, the relationship between IT investment and performance shows improvement. Thus, over time, the performance paradox becomes less pronounced. In some cases, it appears that past investments in IT actually help future performance.
14243133
Generalization Performance in PARSEC - A Structured Connectionist Parsing Architecture
This paper presents PARSEC--a system for generating connectionist parsing networks from example parses. PARSEC is not based on formal grammar systems and is geared toward spoken language tasks. PARSEC networks exhibit three strengths important for application to speech processing: 1) they learn to parse, and generalize well compared to hand-coded grammars; 2) they tolerate several types of noise; 3) they can learn to use multi-modal input. Presented are the PARSEC architecture and performance analyses along several dimensions that demonstrate PARSEC's features. PARSEC's performance is compared to that of traditional grammar-based parsing systems.
5467372
Organizing and Searching the World Wide Web of Facts - Step One: The One-Million Fact Extraction Challenge
Due to the inherent difficulty of processing noisy text, the potential of the Web as a decentralized repository of human knowledge remains largely untapped during Web search. The access to billions of binary relations among named entities would enable new search paradigms and alternative methods for presenting the search results. A first concrete step towards building large searchable repositories of factual knowledge is to derive such knowledge automatically at large scale from textual documents. Generalized contextual extraction patterns allow for fast iterative progression towards extracting one million facts of a given type (e.g., Person-BornIn-Year) from 100 million Web documents of arbitrary quality. The extraction starts from as few as 10 seed facts, requires no additional input knowledge or annotated text, and emphasizes scale and coverage by avoiding the use of syntactic parsers, named entity recognizers, gazetteers, and similar text processing tools and resources.
8533646
Dimensionality reduction using genetic algorithms
Pattern recognition generally requires that objects be described in terms of a set of measurable features. The selection and quality of the features representing each pattern affect the success of subsequent classification. Feature extraction is the process of deriving new features from original features to reduce the cost of feature measurement, increase classifier efficiency, and allow higher accuracy. Many feature extraction techniques involve linear transformations of the original pattern vectors to new vectors of lower dimensionality. While this is useful for data visualization and classification efficiency, it does not necessarily reduce the number of features to be measured since each new feature may be a linear combination of all of the features in the original pattern vector. Here, we present a new approach to feature extraction in which feature selection and extraction and classifier training are performed simultaneously using a genetic algorithm. The genetic algorithm optimizes a feature weight vector used to scale the individual features in the original pattern vectors. A masking vector is also employed for simultaneous selection of a feature subset. We employ this technique in combination with the k nearest neighbor classification rule, and compare the results with classical feature selection and extraction techniques, including sequential floating forward feature selection, and linear discriminant analysis. We also present results for the identification of favorable water-binding sites on protein surfaces.
6932749
Adaptive stream resource management using Kalman Filters
To answer user queries efficiently, a stream management system must handle continuous, high-volume, possibly noisy, and time-varying data streams. One major research area in stream management seeks to allocate resources (such as network bandwidth and memory) to query plans, either to minimize resource usage under a precision requirement, or to maximize precision of results under resource constraints. To date, many solutions have been proposed; however, most solutions are ad hoc with hard-coded heuristics to generate query plans. In contrast, we perceive stream resource management as fundamentally a filtering problem, in which the objective is to filter out as much data as possible to conserve resources, provided that the precision standards can be met. We select the Kalman Filter as a general and adaptive filtering solution for conserving resources. The Kalman Filter has the ability to adapt to various stream characteristics, sensor noise, and time variance. Furthermore, we realize a significant performance boost by switching from traditional methods of caching static data (which can soon become stale) to our method of caching dynamic procedures that can predict data reliably at the server without the clients' involvement. In this work we focus on minimization of communication overhead for both synthetic and real-world streams. Through examples and empirical studies, we demonstrate the flexibility and effectiveness of using the Kalman Filter as a solution for managing trade-offs between precision of results and resources in satisfying stream queries.