1,514 research outputs found
Subjective Sampling Approaches to Resource Estimation
This paper suggests deficiencies in present sampling approaches to regional resource estimation, and ways in which these deficiencies might be remedied. General approaches to resource estimation are discussed, as are requirements which well conceived approaches should satisfy. Using presently available theory, a comprehensive sampling approach to estimation can be formulated. The results of such an analysis are directly incorporable into decisions concerning exploration strategy optimization. However, further computational and experimental work are required before this approach is operational
Comments on Decision Objectives and Attributes for the Nuclear Siting Study
The object of this paper is to summarize discussions at IIASA on attributes or indices for siting decision making. While I have attempted to include differing views on most attributes, I make no pretense of this being an unbiased review.
In addressing decisions of any type and public policy decisions in particular, the choices which one makes of goals, attributes, and normative models fairly well determines a priori what the conclusions will be. It is here that decisions are actually made. Therefore it is absolutely necessary that we be judicious in our selections. In some sense, all that follows these choices is a technical follow through, although this somewhat overstates the point.
The present paper may be summarized as follows. First, a short discussion of goals and attributes is presented; then a set of attributes is listed according to inferred objectives; and finally, each objective and attribute is reviewed and recommendations are made
Spatial Process Modeling for Air Pollution Standards: A Problem Statement
Spatial process models have application to problems in several disciplines. The problem presented here treats monitoring and control of air pollution, but the methodological base seems similar to several other problems, and the hope in outlining this problem is to perhaps generate interest in others working on similar problems, or towards work on this problem itself
Stochastic Tools for Network Intrusion Detection
With the rapid development of Internet and the sharp increase of network
crime, network security has become very important and received a lot of
attention. We model security issues as stochastic systems. This allows us to
find weaknesses in existing security systems and propose new solutions.
Exploring the vulnerabilities of existing security tools can prevent
cyber-attacks from taking advantages of the system weaknesses. We propose a
hybrid network security scheme including intrusion detection systems (IDSs) and
honeypots scattered throughout the network. This combines the advantages of two
security technologies. A honeypot is an activity-based network security system,
which could be the logical supplement of the passive detection policies used by
IDSs. This integration forces us to balance security performance versus cost by
scheduling device activities for the proposed system. By formulating the
scheduling problem as a decentralized partially observable Markov decision
process (DEC-POMDP), decisions are made in a distributed manner at each device
without requiring centralized control. The partially observable Markov decision
process (POMDP) is a useful choice for controlling stochastic systems. As a
combination of two Markov models, POMDPs combine the strength of hidden Markov
Model (HMM) (capturing dynamics that depend on unobserved states) and that of
Markov decision process (MDP) (taking the decision aspect into account).
Decision making under uncertainty is used in many parts of business and
science.We use here for security tools.We adopt a high-quality approximation
solution for finite-space POMDPs with the average cost criterion, and their
extension to DEC-POMDPs. We show how this tool could be used to design a
network security framework.Comment: Accepted by International Symposium on Sensor Networks, Systems and
Security (2017
Standards for Facility Siting: Uncertain Utility in Decision-Making
One approach to regulating private siting decisions is by setting standards on the impacts of large constructed facilities. Theoretical structures of preferences for and among preferences, however, lead to implications which are sometimes overlooked in standard setting. Further, a central issue is that the objective function describing societal preferences is uncertain. Analytically including objective function uncertainty in standard setting allows information from several sources to be quantitatively coalesced, allows allocation decisions for investment in preference assessment to be quantitatively analyzed, and leads to speculations on the handling of temporal changes in preference
A Very low bit-rate speech recognition system
When using extracted speech feature coefficients for speech synthesis, quantization is considered a lossy compression scheme. The data being compressed cannot be recovered or reconstructed exactly. However, in a speech recognition system for command and control purposes, a certain amount of quantization can be allowed, with comparable results. In some cases, quantization even serves to close the gaps between the coefficients of the incoming speech signal and those of the templates. Since the coefficients are not being used to reconstruct the signal, a very coarse quantization can be used, enabling a very low bit-rate transmission with very good recognition results. To reduce the bandwidth further, a binary coding procedure, such as Huffman or Arithmetic Coding, can be applied to the quantized coefficients. Upon receipt of the transmission, the quantized coefficients are decoded and used to perform speech recognition. The sets of coefficients are compared to the templates for each of the commands in the vocabulary. Speech, however, is dynamic in nature and a dynamic recognition procedure is needed to allow for different vocal inflections and durations. A procedure called Dynamic Time Warping is used to warp the time axis of the templates to more closely fit the information coming in. By combining all these techniques, a very accurate, very low bit-rate recognizer has been developed and is discussed in this paper
Probability Theory in Geological Exploration
The purpose of this review is to summarize briefly certain applications of probability theory and statistics to geological exploration and inference, and in particular to problems of economic geology. The hope is that this summary will be of use in planning the IIASA conference on resource estimation scheduled for May, 1975, and will provide a reference within which to review certain contributions to that conference
Extensions of K.J. Roy's and R.E. Roadifer's Subjective Approach to Oil Resource Estimation
Messrs. Roy and Roadifer, in separate papers, have proposed similar methodologies for making regional resource estimates. The idea underlying their approach is quite interesting, and may have extensions incorporable in larger schemes of estimation.
Roy's and Roadifer's approach adopts a simple "structural" model of deposit size, and uses the subjective feelings of geologists to generate probability density functions (pdf's) of the model parameters. A pdf of regional resources is approximated by using Monte Carlo simulation
Sampling for Group Utility
A sampling theory approach is developed for estimating group utility functions for inclusion in decision-analytic approaches to public plan evaluation. This approach is based. on Bayesian sampling theory and leads to estimates of group utility accounting for sampling and measurement error. The results of the estimation may be directly incorporated in decision analysis. The strength of this approach is that it leads to more rigorously based estimates of interest group utility functions than commonly used. surrogates, and can be analytically balanced with other forms of preference information such as market data
Study Abroad in Teacher Education
Introduction to Special Themed Issue: Study Abroad in Teacher Educatio
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